A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

The invention provides a face image correction method. The face image correction method comprises steps that an original image sent by a sending end is received, the original image is detected, a face image is acquired through interception, characteristic points of the face image are positioned, at least two face characteristic point position coordinates are generated, a face deflection angle is calculated based on the at least two face characteristic point position coordinates, reverse rotation of the face image is carried out based on the face deflection angle, the face image after reverse rotation is detected, and the face correction image is acquired through interception. The deflected face image of the image is corrected before face identification, namely, reverse rotation of the face image based on the face deflection angle in the deflection direction, the image after rotation is detected, and the face correction image is acquired through interception. As the face correction image has no deflection, face identification accuracy can be improved, plenty of non-face areas are removed when the face correction image is acquired through interception, and workload during subsequent image processing can be reduced.

Face image correction method

Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.

/pdf/distortion-specific-feature-selection-algorithm-for-214omf5xkp.pdf

Distortion-specific feature selection algorithm for universal blind image quality assessment

An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.

/pdf/prospects-of-structural-similarity-index-for-medical-image-25zad8js.pdf

Prospects of Structural Similarity Index for Medical Image Analysis

https://iopscience.iop.org/article/10.1088/1757-899X/557/1/012027/pdf

Image Enhancement Based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT): A Review

Perceptual image quality assessment (IQA) adopts a computational model to assess the image quality in a fashion, which is consistent with human visual system (HVS). From the view of HVS, different image regions have different importance. Based on this fact, we propose a simple and effective method based on the image decomposition for image quality assessment. In our method, we first divide an image into two components: edge component and texture component. To separate edge and texture components, we use the TV flow-based nonlinear diffusion method rather than the classic TV regularization methods, for highly effective computing. Different from the existing content-based IQA methods, we realize different methods on different components to compute image quality. More specifically, the luminance and contrast similarity are computed in texture component, while the structural similarity is computed in edge component. After obtaining the local quality map, we use texture component again as a weight function to derive a single quality score. Experimental results on five datasets show that, compared with previous approaches in the literatures, the proposed method is more efficient and delivers higher prediction accuracy.

/pdf/image-decomposition-based-structural-similarity-index-for-4cn23luus2.pdf

Image decomposition-based structural similarity index for image quality assessment

In this paper, we propose a wavelet-based illumination normalization method for face recognition against different directions and strength of light. Here, by one-level discrete wavelet transform, a given face image is first decomposed into low frequency and high frequency components, respectively, and then the two components are processed separately through contrast enhancement to eliminate the effect of illumination variations and enhance the detailed edge information. Finally the normalized image is obtained through the inverse discrete wavelet transform. Experimental results on the Yale B, the extended Yale B and CMU PIE face databases show that the proposed method can effectively reduce the effect of illumination variations on face recognition.

A Wavelet-Based Image Preprocessing Method or Illumination Insensitive Face Recognition.

A local feature descriptor, the local gradient binary count pattern (LGBCP), is proposed for face rec- ognition. Unlike some current methods that extract features directly from a face image in the spatial domain, LGBCP encodes the local gradient information of the face's texture in an effective way and provides a more discriminative code than other methods. We compute the gradient information of a face image through convo- lutions with compass masks. The gradient information is encoded using the local binary count operator. We divide a face into several subregions and extract the distribution of the LGBCP codes from each subregion. Then all the histograms are concatenated into a vector, which is used for face description. For recognition, the chi-square statistic is used to measure the similarity of different feature vectors. Besides directly calculating the similarity of two feature vectors, we provide a weighted matching scheme in which different weights are assigned to different subregions. The nearest-neighborhood classifier is exploited for classification. Experiments are conducted on the FERET, CAS-PEAL, and AR face databases. LGBCP achieves 96.15% on the Fb set of FERET. For CAS-PEAL, LGBCP gets 96.97%, 98.91%, and 90.89% on the aging, distance, and expression sets, respectively. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.6.063003)

Face recognition using local gradient binary count pattern

The invention discloses a human face posture correction method, and the method comprises the steps: obtaining the coordinates of the central point of a to-be-recognized human face image; carrying out the positioning of human eyes in a specified region of the image and calculating the position coordinates and the central coordinates of a connection line of two eyes; calculating a human face deflection angle; calculating the coordinates of two eyes of the image after calculation, and cutting an effective human face region for subsequent processing according to the coordinates of two eyes, thereby achieving the effect of correcting human face posture. The method effectively improves the accuracy of human face recognition, and enables the human face recognition to be more practical.

Human face posture correction method

The illumination problem is one of the main bottlenecks in a practical face recognition system. Illumination preprocessing is an effective way to handle lighting variations for robust face recognition. In this paper, we present a novel illumination insensitive image, namely directional gradients integration image (DGII), for illumination insensitive face recognition. Unlike the existing model-based methods, the DGII is generated directly from the decomposed gradient components of a logarithmic image, without involving any training procedure. Based on the Lambertian reflectance model, we first calculate the horizontal and vertical gradients in the logarithmic domain to eliminate the illumination component. Secondly, to utilize the gradient orientation information, the two gradients are further decomposed into four components along four directions. Then, the four directional gradients are integrated to reconstruct an illumination insensitive image using anisotropic diffusion. Finally, the reconstructed image is fused with the gradient magnitude image through weighted summing. For performance evaluation, we simply use principal component analysis for feature extraction, Euclidean distance as similarity measure and nearest-neighbor classifier for face recognition. Experiments on the Yale B, the extended Yale B and the CMU PIE (The Carnegie Mellon University pose, illumination and expression database) face databases show that the proposed method provides better results than some state-of-the-art methods, showing its effectiveness for illumination normalization.

Xiaochao Zhao

Papers

Image decomposition-based structural similarity index for image quality assessment

A Wavelet-Based Image Preprocessing Method or Illumination Insensitive Face Recognition.

Face recognition using local gradient binary count pattern

Human face posture correction method

Directional gradients integration image for illumination insensitive face representation