Showing papers on "Image gradient published in 2006"

Finding People in Images and Videos

Abstract: This thesis targets the detection of humans and other object classes in images and videos. Our focus is on developing robust feature extraction algorithms that encode image regions as highdimensional feature vectors that support high accuracy object/non-object decisions. To test our feature sets we adopt a relatively simple learning framework that uses linear Support Vector Machines to classify each possible image region as an object or as a non-object. The approach is data-driven and purely bottom-up using low-level appearance and motion vectors to detect objects. As a test case we focus on person detection as people are one of the most challenging object classes with many applications, for example in film and video analysis, pedestrian detection for smart cars and video surveillance. Nevertheless we do not make any strong class specific assumptions and the resulting object detection framework also gives state-of-the-art performance for many other classes including cars, motorbikes, cows and sheep. This thesis makes four main contributions. Firstly, we introduce grids of locally normalised Histograms of Oriented Gradients (HOG) as descriptors for object detection in static images. The HOG descriptors are computed over dense and overlapping grids of spatial blocks, with image gradient orientation features extracted at fixed resolution and gathered into a highdimensional feature vector. They are designed to be robust to small changes in image contour locations and directions, and significant changes in image illumination and colour, while remaining highly discriminative for overall visual form. We show that unsmoothed gradients, fine orientation voting, moderately coarse spatial binning, strong normalisation and overlapping blocks are all needed for good performance. Secondly, to detect moving humans in videos, we propose descriptors based on oriented histograms of differential optical flow. These are similar to static HOG descriptors, but instead of image gradients, they are based on local differentials of dense optical flow. They encode the noisy optical flow estimates into robust feature vectors in a manner that is robust to the overall camera motion. Several variants are proposed, some capturing motion boundaries while others encode the relative motions of adjacent image regions. Thirdly, we propose a general method based on kernel density estimation for fusing multiple overlapping detections, that takes into account the number of detections, their confidence scores and the scales of the detections. Lastly, we present work in progress on a parts based approach to person detection that first detects local body parts like heads, torso, and legs and then fuses them to create a global overall person detector.

A morphological gradient approach to color edge detection

Abstract: A new color edge detector based on vector differences is proposed. The basic technique gives as its output the maximum distance between the vectors within a mask. When applied to scalar-valued images, the method reduces to the classic morphological gradient. The technique is relatively computationally efficient and can also be readily applied to other vector-valued images. To improve the performance in the presence of noise, a novel pairwise outlier rejection scheme is employed. A quantitative evaluation using Pratt's figure of merit shows the new technique to outperform other recently proposed color edge detectors. In addition, application to real images demonstrates the approach to be highly effective despite its low complexity.

Comparative Study of Edge Detection Algorithms Applying on the Grayscale Noisy Image Using Morphological Filter

Abstract: In this paper, classified and comparative study of edge detection algorithms are presented. Experimental results prove that Boie-Cox, ShenCastan and Canny operators are better than Laplacian of Gaussian (LOG), while LOG is better than Prewitt and Sobel in case of noisy image. Subjective and objective methods are used to evaluate the different edge operators. The morphological filter is more important as an initial process in the edge detection for noisy image and used opening-closing operation as preprocessing to filter noise. Also, smooth the image by first closing and then dilation to enhance the image before the edge operators affect.

Image denoising using total least squares

Abstract: In this paper, we present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed noise. An image patch from an ideal image is modeled as a linear combination of image patches from the noisy image. We propose to fit this model to the real-world image data in the total least square (TLS) sense, because the TLS formulation allows us to take into account the uncertainties in the measured data. We develop a method to reduce the contribution from the irrelevant image patches, which will sharpen the edges and reduce edge artifacts at the same time. Although the proposed algorithm is computationally demanding, the image quality of the output image demonstrates the effectiveness of the TLS algorithms

Fusion of sensor data and synthetic data to form an integrated image

Abstract: A system and method for fusing sensor data and synthetic data to form an integrated image are described. An image registration process is used to fuse the images. The image registration process is performed on gradients of a landmark image. The gradients are extracted from both sensor and synthetic datasets for the landmark image. Using the image gradient, a center of mass for each of the gradient's curves is calculated. By solving a minimization problem to reduce error, a desired transformation operator can be obtained to match the sensor image to the synthetic image.

Image Segmentation Based on Watershed and Edge Detection Techniques

Abstract: A combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map was used to perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain an accurate edge maps of our images without using watershed method. In this paper: We solved the problem of undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image and the final edge detection result is one closed boundary per actual region in the image.

Image warping and lateral color correction

Abstract: Color or grayscale images having optical elements induced geometric distortions can be corrected on individual color image component by creating correction image component having the complementary distortion; and applying the correction image component to the corresponding distorted color image component.

A Confidence Measure for Variational Optic flow Methods

Abstract: In this paper we investigate the usefulness of confidence measures for variational optic flow computation. To this end we discuss the frequently used sparsification strategy based on the image gradient. Its drawbacks motivate us to propose a novel, energy-based confidence measure that is parameter-free and applicable to the entire class of energy minimising optic flow techniques. Experimental evaluations show that this confidence measure leads to excellent results, independently of the image sequence or the underlying variational approach.

Shadow Removal from a Single Image

Abstract: Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, it is always hard to provide a robust system to eliminate shadows in static images. This paper aimed to give a comprehensive method to remove both vague and hard shadows from a single image. First, classification is applied to the derivatives of the input image to separate the vague shadows. Then, color invariant is exploited to distinguish the hard shadow edges from the material edges. Next, we derive the illumination image via solving the standard Poisson equation. Finally, we got the shadow-free reflectance image. Experimental results showed that our method can robustly remove both vague and hard shadows appearing in the real scene images.

Performance Evaluation of Prewitt Edge Detector for Noisy Images

Abstract: Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper evaluates the performance of Prewitt Edge Detector for detection of edges in digital images corrupted with different kinds of noise. Different kinds of noise are studied in order to evaluate the performance of the Prewitt Edge Detector. Further, the various standard test Images are examined to validate our results. The software is developed using MATLAB 7.0. 1 It has been observed that the Prewitt Edge Detector works effectively for the digital images corrupted with Poisson Noise where as its performances reduces sharply for other kinds of noise in digital images. The results of this study are quite promising.

Image contrast enhancement

Abstract: A method for contrast enhancement for digital images, including filtering an original image having original color values, to generate a filtered image, receiving parameters for a response curve, the response curve being a function of color value that is user-adjustable, deriving local multipliers by applying the response curve to the filtered image, multiplying the original color values by the local multipliers, thereby generating a contrast-enhanced image from the original image. A system and a computer-readable storage medium are also described.

Edge Suppression by Gradient Field Transformation Using Cross-Projection Tensors

Abstract: We propose a new technique for edge-suppressing operations on images. We introduce cross projection tensors to achieve affine transformations of gradient fields. We use these tensors, for example, to remove edges in one image based on the edge-information in a second image. Traditionally, edge suppression is achieved by setting image gradients to zero based on thresholds. A common application is in the Retinex problem, where the illumination map is recovered by suppressing the reflectance edges, assuming it is slowly varying. We present a class of problems where edge-suppression can be a useful tool. These problems involve analyzing images of the same scene under variable illumination. Instead of resetting gradients, the key idea in our approach is to derive local tensors using one image and to transform the gradient field of another image using them. Reconstructed image from the modified gradient field shows suppressed edges or textures at the corresponding locations. All operations are local and our approach does not require any global analysis. We demonstrate the algorithm in the context of several applications such as (a) recovering the foreground layer under varying illumination, (b) estimating intrinsic images in non-Lambertian scenes, (c) removing shadows from color images and obtaining the illumination map, and (d) removing glass reflections.

Fast Separation of Reflection Components using a Specularity-Invariant Image Representation

Abstract: In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.

Image processing apparatus, image processing method, and computer product

Abstract: When first image data, which is in a first color space, is converted into second image data, which is in a second color space corresponding to an image output unit. Upon converting the image data, if the first image data is color data, background removal process is switched OFF. Otherwise, the background removal process is switched ON. Thus, it is possible to obtain an image desired by a user.

Image sensing apparatus and method for synthesizing a composite image

Abstract: A composite image having a natural appearance is obtained when a synthesis target image is combined with a background image. By photographing a subject under different photographic conditions, a plurality of synthesis target images are obtained and the resulting image data is stored beforehand in a synthesis target image memory. A background image is acquired by photography and a suitable synthesis target image to be combined with the background image is selected by a circuit which searches synthesis target images. A color correction is applied to the selected synthesis target image by an image processing circuit and the synthesis target image following the color correction is combined with the background image. Since a synthesis target image suitable for combination with a background image is selected and then is subjected to a color correction and image synthesis processing, a composite image having a natural appearance is obtained.

Cross Dissolve Without Cross Fade: Preserving Contrast, Color and Salience in Image Compositing

Abstract: Linear interpolation is the standard image blending method used in image compositing. By averaging in the dynamic range, it reduces contrast and visibly degrades the quality of composite imagery. We demonstrate how to correct linear interpolation to resolve this longstanding problem. To provide visually meaningful, high level control over the compositing process, we introduce three novel image blending operators that are designed to preserve key visual characteristics of their inputs. Our contrast preserving method applies a linear color mapping to recover the contrast lost due to linear interpolation. Our salience preserving method retains the most informative regions of the input images by balancing their relative opacity with their relative saliency. Our color preserving method extends homomorphic image processing by establishing an isomorphism between the image colors and the real numbers, allowing any mathematical operation defined on real numbers to be applied to colors without losing its algebraic properties or mapping colors out of gamut. These approaches to image blending have artistic uses in image editing and video production as well as technical applications such as image morphing and mipmapping. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation

Image Processing Method, Image Processing Program, and Image Processing Device

Abstract: There is provided an image processing method for creating a fusion image with automatic and high superposition accuracy. The image processing method includes: (a) a voxel normalization step for generating a first normalized 3D image corresponding to a first 3D image and a second normalized 3D image corresponding to a second 3D image by making the voxel size and the voxel quantity the first 3D image based on a plurality of first tomograms obtained from an arbitrary portion of an examinee identical to those of the second 3D image based on a plurality of second tomograms obtained from the same portion in the valid field of view; and (b) a fusion image generation step for generating a fusion image by using the first normalized 3D image and the second normalized 3D image.

Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation

Abstract: In this paper, a method for detecting possible presence of abnormality in the endoscopic images is presented. The pre-processed endoscopic color images are segmented in the HSI color space. The pixels in the input color image corresponding to the segmented image are extracted for further processing. This image is smoothened using average filter and converted into grayscale image. Its inverse transform is obtained for further processing and extended minima is imposed on the processed image using morphological reconstruction. Then the morphological watershed segmentation is carried out on this image and the number of regions is counted and is compared with the threshold value. If the number of regions is more than the threshold value, then the output image is an indicative of possible presence of abnormality in the image.

Depth dependent filtering of image signal

Abstract: A method and apparatus for rendering of image data for a multi-view display, such as image data for a lenticular auto-stereoscopic display, is disclosed. The method comprises the steps of receiving an image signal representing a first image, the first image comprising 3D image data, and spatially filtering the first image signal to provide a second image signal. The second image signal represents a second image, the spatial filtering being, e.g., a low-pass filter, a high-pass filter or a combination of a low-pass and a high-pass filter. A strength of the spatial filter is determined by a reference depth of the first image and a depth of an image element of the first image. The second image is sampled to a plurality of sub-images, each sub-image being associated with a view direction of the image.

Representing and reconstructing high dynamic range images

Abstract: A high dynamic range image can be recovered from a full-resolution lower-dynamic-range image and a reduced-resolution higher-dynamic-range image. Information regarding higher spatial frequencies may be obtained by extracting high spatial frequencies from the lower-dynamic-range image. In some embodiments an approximate impulse-response function is determined by comparing the higher- and lower-dynamic range images. A scaling image obtained by applying the impulse-response function to a high-frequency band of the lower-dynamic range image is combined with an upsampled higher-dynamic range image to yield a reconstructed image.

Image processing apparatus, image display apparatus, and image display method

Abstract: A maximum and minimum color information detector detects a maximum color-signal gradation level or a value equivalent to the maximum gradation level and a minimum color-signal gradation level or a value equivalent to the minimum gradation level as color information for an image signal input to the image processing apparatus, a correction parameter generator sets correction parameters according to the color information about the input image signal, and a gradation corrector corrects the gradation scale of each color component of the input image signal according to the correction parameters. Contrast can thereby be improved without excessive color collapse.

An Improved Ostu Method for Image Segmentation

Abstract: The Ostu method is one of the applied methods of image segmentation in selecting threshold automatically for its simple calculation and good adaptation. This paper describes an improved method of threshold selecting, through the gray level and gradient mapping (GGM) function. The result of simulation demonstrates that, the new algorithm described in this paper can detect the object and restrain the noise effectively. The algorithm has a widespread application prospect

The Bayesian Operating Point of the Canny Edge Detector

Abstract: We have investigated the operating point of the Canny edge detector which minimizes the Bayes risk of misclassification. By considering each of the sequential stages which constitute the Canny algorithm, we conclude that the linear filtering stage of Canny, without postprocessing, performs very poorly by any standard in pattern recognition and achieves error rates which are almost indistinguishable from a priori classification. We demonstrate that the edge detection performance of the Canny detector is due almost entirely to the postprocessing stages of nonmaximal suppression and hysteresis thresholding