Color Distribution Information Research Articles

Highlighted Document Image Classification

There are many existing document image classification researches, but most of them are not designed for use in constrained computer resources, like printers, or focused on documents with highlighter pen marks. To enable printers to better discriminate highlighted documents, we designed a set of features in CIE Lch(a* b*) space to use along with the support vector machine. The features include two gamut-based features and six low-level color features. By first identifying the highlight pixels, and then computing the distance from the highlight pixels to the boundary of the printer gamut, the gamut-based features can be obtained. The low-level color features are built upon the color distribution information of the image blocks. The best feature subset of the existing and new features is constructed by sequential forward floating selection (SFFS) feature selection. Leave-one-out cross-validation is performed on a dataset with 400 document images to evaluate the effectiveness of the classification model. The cross-validation results indicate significant improvements over the baseline highlighted document classification model.

Surface color distribution analysis by computer vision compared to sensory testing: Vacuum fried fruits as a case study

Color is a main factor in the perception of food product quality. Food surfaces are often not homogenous at micro-, meso-, and macroscopic scales. This matrix can include a variety of colors that are subject to changes during food processing. These different colors can be analyzed to provides more information than the average color. The objective of this study was to compare color analysis techniques on their ability to differentiate samples, quantify heterogeneity, and flexibility. The included techniques are sensory testing, Hunterlab colorimeter, a commercial CVS (IRIS-Alphasoft), and the custom made CVS (Canon-CVS) in analyzing nine different vacuum fried fruits. Sensory testing was a straightforward method and able to describe color heterogeneity. However, the subjectivity of the panelist is a limitation. Hunterlab was easy and accurate to measure homogeneous samples with high differentiation, without the color distribution information. IRIS-Alphasoft was quick and easy for color distribution analysis, however the closed system is the limit. The Canon-CVS protocol was able to assess the color heterogeneity, able to discriminate samples and flexible. As a take home massage, objective color distribution analysis has a potential to unlock the limitation of traditional color analysis by providing more detailed color distribution information which is important with respect to overall product quality.

A Single Image 3D Reconstruction Method Based on a Novel Monocular Vision System.

Three-dimensional (3D) reconstruction and measurement are popular techniques in precision manufacturing processes. In this manuscript, a single image 3D reconstruction method is proposed based on a novel monocular vision system, which includes a three-level charge coupled device (3-CCD) camera and a ring structured multi-color light emitting diode (LED) illumination. Firstly, a procedure for the calibration of the illumination’s parameters, including LEDs’ mounted angles, distribution density and incident angles, is proposed. Secondly, the incident light information, the color distribution information and gray level information are extracted from the acquired image, and the 3D reconstruction model is built based on the camera imaging model. Thirdly, the surface height information of the detected object within the field of view is computed based on the built model. The proposed method aims at solving the uncertainty and the slow convergence issues arising in 3D surface topography reconstruction using current shape-from-shading (SFS) methods. Three-dimensional reconstruction experimental tests are carried out on convex, concave, angular surfaces and on a mobile subscriber identification module (SIM) card slot, showing relative errors less than 3.6%, respectively. Advantages of the proposed method include a reduced time for 3D surface reconstruction compared to other methods, demonstrating good suitability of the proposed method in reconstructing surface 3D morphology.

Object Tracking Algorithm Based on Dual Color Feature Fusion with Dimension Reduction.

Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios.

Encoding brain network response to free viewing of videos.

A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain's responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain's activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.

알약 인식을 위해 색 특징정보를 이용한 CBIRS/TB

This thesis proposes CBIRS/TB method that uses a tablet's color distribution information and form distinctive in content-based search. CBIRS/TB can avoid misuses and improper tablet uses by conducting content-based search in commonly prescribed tablets. The existing FE-CBIRS system is limited to recognizing only the image of color and shape of the tablet, that leads to applying insufficient form-specific information. While CBIRS/TB utilizes average, standard deviation, hue and saturation of each tablets in color, brightness, and contrast, FE-CBIRS has partial-sphere application problem; only applying the typical color of the tablet. Also, in case of the ∙제1저자 : 구건서 ∙투고일 : 2014. 1. 9, 심사일 : 2014. 1. 22, 게재확정일 : 2014. 2. 6. * 숭의여자대학교 디지털미디어전공 교수(Dept. Digital Media, Soong Eui Women's College) ※ 본 논문은 2013년도 숭의여자대학교 교내 학술 연구비 지원에 의한 것임. 50 Journal of The Korea Society of Computer and Information February 2014 shape-specific-information, Invariant Moment is mainly used for the extracted partial-spheres. This causes delayed processing time and accuracy problems. Therefore, to improve this setback, this thesis indexed color-specific-information of the extracted images into categorized classification for improved search speed and accuracy. ▸

Detection of Moving Objects with Fuzzy Color Coherence Vector

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.

An Effective Similarity Measurement Algorithm for Dominant Color Feature Matching in Image Retrieval

For the reason that dominant colors can characterize color information of image region and can represent the image using fewer dimensions, it is one of the widely used color features in image retrieval. We extract the dominant color feature in HSV color space, and combine it with color distribution information. In this paper, a new similarity measurement algorithm based on block distance is proposed for dominant color matching. Our proposed algorithm not only takes the distance between dominant colors into account, but also the difference of the percentage of dominant colors. The average precision of our algorithm improves about 5% and about 14% respectively compared with block distance and Euclidean distance. Although the average precision of our algorithm is almost equal to quadratic form distance, the computation cost of our algorithm is obviously less than it.

Spatial color histogram based center voting method for subsequent object tracking and segmentation

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.

Color Distribution Information for the Reduced-Reference Assessment of Perceived Image Quality

Reduced-reference systems can predict in real-time the perceived quality of images for digital broadcasting, only requiring that a limited set of features, extracted from the original undistorted signals, is transmitted together with the image data. This paper uses descriptors based on the color correlogram, analyzing the alterations in the color distribution of an image as a consequence of the occurrence of distortions, for the reduced reference data. The processing architecture relies on a double layer at the receiver end. The first layer identifies the kind of distortion that may affect the received signal. The second layer deploys a dedicated prediction module for each type of distortion; every predictor yields an objective quality score, thus completing the estimation process. Computational-intelligence models are used extensively to support both layers with empirical training. The double-layer architecture implements a general purpose image quality assessment system, not being tied up to specific distortions and, at the same time, it allows us to benefit from the accuracy of specific, distortion-targeted metrics. Experimental results based on subjective quality data confirm the general validity of the approach.

색상 분포 및 인체의 상황정보를 활용한 다중카메라 기반의 사람 대응

In this paper, we proposed a method which corresponds people under the structured spaces with multiple cameras. The correspondence takes an important role for using multiple camera system. For solving this correspondence, the proposed method consists of three main steps. Firstly, moving objects are detected by background subtraction using a multiple background model. The temporal difference is simultaneously used to reduce a noise in the temporal change. When more than two people are detected, those detected regions are divided into each label to represent an individual person. Secondly, the detected region is segmented as features for correspondence by a criterion with the color distribution and context information of human body. The segmented region is represented as a set of blobs. Each blob is described as Gaussian probability distribution, i.e., a person model is generated from the blobs as a Gaussian Mixture Model (GMM). Finally, a GMM of each person from a camera is matched with the model of other people from different cameras by maximum likelihood. From those results, we identify a same person in different view. The experiment was performed according to three scenarios and verified the performance in qualitative and quantitative results.

Synopsis of the Eastern North American Species of the Plant Bug Genus Parthenicus, with Descriptions of Three New Species and a Revised Key (Heteroptera: Miridae: Orthotylinae)

Three new species of the orthotyline genus Parthenicus Reuter are described and the seven previously known species of eastern North America are diagnosed. Parthenicus cruentus, n.sp. is described from Nebraska, P. sedumicola, n.sp. from Arkansas, and P. wheeleri, n.sp. from Oklahoma and Texas. A lectotype from Texas is designated and male genitalia are illustrated for P. psalliodes Reuter, the type species of the genus. Also provided are selected scanning electron photomicrographs and illustrations of the male genitalia for new species, a color habitus illustration for P. sedumicola, and for all species a color dorsal habitus photograph, hosts, and distribution information. A revised identification key to the 10 eastern species is given to help distinguish species.

Color-texture based unsupervised segmentation using JSEG with fuzzy connectedness

Abstract Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven't been heavily blurred near boundaries of underlying regions.

Block Based Face Detection Scheme Using Face Color and Motion Information

In a sequence of images obtained by surveillance cameras, facial regions appear very small and their colors change abruptly by lighting condition. This paper proposes a new face detection scheme, robust on complex background, small size, and lighting conditions. The proposed method is consisted of three processes. In the first step, the candidates for the face regions are selected using face color distribution and motion information. In the second stage, the non-face regions are removed using face color ratio, boundary ratio, and average of column-wise intensity variation in the candidates. The face regions containing eyes and mouth are segmented and classified, and then they are scored using their topological relations in the last step. To speed up and improve a performance the above process, a block based image segmentation technique is used. The experiments have shown that the proposed algorithm detects faced regions with more than 91% of accuracy and less than 4.3% of false alarm rate.

Modeling the sensitivity of moment invariants in a recognition system

We derive a sensitivity analysis for moment invariants of multidimensional distributions. These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in color images. In this context, the sensitivity analysis predicts the response of moment invariants to partial occlusion. Using the results of the sensitivity analysis, we develop a novel surface representation called the invariant profile, which captures color distribution and spatial information while remaining invariant to the spectral content of the scene illumination. Unlike for previous representations, the recognition of invariant profiles does not require illumination correction. We demonstrate the sensitivity analysis and the use of invariant profiles for recognition with a set of experiments on color images.