Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

A system for recognition and description of graphics

Abstract: A system for automatic recognition and description of graphical primitives and their interconnections in a mixed text/graphics image is described. The input to the system is a high-resolution binary image obtained by scanning paper-based documents. The Hough transform is applied to the connected components of the image to locate and separate text strings of various font size and orientation. The graphics in the segmented image is processed to represent thin entities by their core lines and thick objects by their boundaries. Lines of various types are identified and segmented into straight line and curved line segments during this process. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Various hatching and filling patterns in the image are identified and described. The algorithm has been implemented as software, and its robustness and performance have evaluated tested using a number of test images. The results for a simple test image are reported. >

Method for identifying target based on dimension reduction local feature descriptor and hidden conditional random field

Abstract: The invention provides a method for identifying a target based on a dimension reduction local feature descriptor and hidden conditional random field The method is to establish a target identification model for identifying an object, and the model establishing process is a process that the model performs supervised training by using a training image as a sample, wherein each object in the training image corresponds to different label values The method comprises the following steps of: calculating a descriptor vector of SIFT (Scale invariant feature transform) for the training images of different objects, wherein the descriptor vector corresponding to each image forms a corresponding high-dimensional vector set; performing dimension reduction on the SIFT set by adopting a Neighbor Preserving Embedding (NPE) method; and allowing the vector group subjected to dissension reduction and a label of the object corresponding to a source image to form a dualistic group, namely, each image has a corresponding dualistic group, and the set consisting of the dualistic groups can be used as a sample for training a hidden conditional random field model An identifying process by the model, namely for a set test image comprises the following steps of: calculating the SIFT feature descriptor set of the test image; reducing dimension of the acquired SIFT set by the NPE method; inputting the vector set subjected to dimension reduction to the hidden conditional random field acquired by training; and outputting the final object label serving as an identification result

Open Hand Detection in a Cluttered Single Image using Finger Primitives

Abstract: Hand Detection plays an important role in human computer interaction (HCI) applications, as well as surveillance. We propose a hand detection technique that is robust to different skin color, illumination and shadow irregularities by exploiting the geometric properties of the hand. We first obtain the responses from two detectors that operate independently on the test image to identify parallel finger edges and curved fingertips. These responses are then grouped by using two decision trees trained on each primitive class, yielding two separate collections of groups. The final merging algorithm returns candidate hands in a given single image by comparing groups across each collection and merging those that satisfy a scoring function. The proposed system is robust to the size and the orientation of the hand, with the single requirement that one or more fingers are visible. The system is the first to successfully detect hands in an uncontrolled environment, without training on the skin color within a single image or using motion information.

New developments in color image tampering detection

Abstract: In this paper, an efficient framework for passive-blind color image tampering detection is presented. Statistical features are extracted from a given test image and a set of 2-D arrays derived by applying multi-size block discrete cosine transform to the given test image. Image features are extracted from Cr channel, a chroma channel in YCbCr color space, because of its observed sensitivity to color image tampering. A support vector machine is employed to evaluate the effectiveness of image features over a color image dataset recently established for tampering detection. Boosting feature selection is applied to having feature dimensionality reduced so as to make detection accuracy generalizable and computational complexity decreased. Experimental results have demonstrated that the proposed framework applied to the aforementioned dataset outperforms the state of the arts by distinct margins.