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 author begins by discussing the image formation process. The demosaicking methods are examined in three groups: the first group consists of heuristic approaches. The second group formulates demosaicking as a restoration problem. The third group is a generalization that uses the spectral filtering model given in Wandell.

Demosaicking: color filter array interpolation

In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteriori estimation technique by minimizing a multiterm cost function. The L/sub 1/ norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.

/pdf/multiframe-demosaicing-and-super-resolution-of-color-images-2bvmrw3zm4.pdf

Multiframe demosaicing and super-resolution of color images

Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array
(CFA) samples Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing
of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source
codes/executables in response to the principle of "reproducible research" In this article, we provide a systematic
survey of over seventy published works in this field since 1999 (complementary to previous reviews22, 67)
Our review attempts to address important issues to demosaicing and identify fundamental differences among
competing approaches Our findings suggest most existing works belong to the class of sequential demosaicing
- ie, luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered
luminance information We report our comparative study results with a collection of eleven competing
algorithms whose source codes or executables are provided by the authors Our comparison is performed on
two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging) While
most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on
the IMAX one (images with varying-hue and high-saturation edges) degrades significantly Such observation
suggests the importance of properly addressing the issue of mismatch between assumed model and observation
data in demosaicing, which calls for further investigation on issues such as model validation, test data selection
and performance evaluation

http://www4.comp.polyu.edu.hk/~cslzhang/paper/conf/demosaicing_survey.pdf

Image demosaicing: a systematic survey

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.

/pdf/linear-demosaicing-inspired-by-the-human-visual-system-3a4iil5xvg.pdf

Linear demosaicing inspired by the human visual system

Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods. The proposed demosaicking method consists of two successive steps: an interpolation step that estimates missing color values by exploiting spatial and spectral correlations among neighboring pixels, and a post-processing step that suppresses noticeable demosaicking artifacts by adaptive median filtering. Moreover, in recognition of the limitations of current image measures, we propose two types of image measures to quantify the performance of different demosaicking methods; the first type evaluates the fidelity of demosaicked images by computing the peak signal-to-noise ratio and CIELAB /spl utri/E/sup *//sub ab/ for edge and smooth regions separately, and the second type accounts for one major demosaicking artifact-zipper effect. We gauge the proposed demosaicking method and image measures using several existing methods as benchmarks, and demonstrate their efficacy using a variety of test images.

/pdf/color-filter-array-demosaicking-new-method-and-performance-1qksa8f2t3.pdf

Color filter array demosaicking: new method and performance measures

A system using color histogram based recognition technique is presented for tracking of moving people. The proposed system aims to resolve the identity of each tracked person after an occlusion, which is a common problem encountered in tracking of multiple objects. The system recognizes and keeps track of moving people with the aid of their color histograms. To make the recognition more robust against change of background illumination, a shadow-removal scheme is designed to select only those pixels belonging to the tracked people for constructing their color histograms. Experimental results of real video data are reported to show the efficacy of the proposed system.

A color histogram based people tracking system

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies.

We present in this paper a vision-based approach to detection of drowning incidents in swimming pools at the earliest possible stage. The proposed approach consists of two main parts: a vision component which can reliably detect and track swimmers in spite of large scene variations of monitored pool areas, and an event-inference module which parses observation sequences of swimmer features for possible drowning behavioral signs. The vision component employs a model-based approach to represent and differentiate the background pool areas and foreground swimmers. The event-inference module is constructed based on a finite state machine, which integrates several reasoning rules formulated from universal motion characteristics of drowning swimmers. Possible drowning incidents are quickly detected using a sequential change detection algorithm. We have applied the proposed approach to a number of video clips of simulated drowning and obtained promising results as reported in this paper.

A vision-based approach to early detection of drowning incidents in swimming pools

Segmenting articular cartilage and meniscus from magnetic resonance (MR) images is an essential task for the assessment of knee pathology. Most of the previous classification-based works for cartilage and meniscus segmentation only rely on independent labellings by a classifier, but do not consider the spatial context interaction. The labels of most image voxels are actually dependent upon their neighbours. In this study, we present an automatic knee segmentation system working on multi-contrast MR images where a novel classification model unifying an extreme learning machine (ELM)-based association potential and a discriminative random field (DRF)-based interaction potential is proposed. The DRF model introduces spatial dependencies between neighbouring voxels to the independent ELM classification. We exploit a rich set of features From multi-contrast MR images to train the proposed classification model and perform the loopy belief propagation for the inference. The proposed model is evaluated on multi-contrast MR datasets acquired from 11 subjects with results outperforming the independent classifiers in terms of segmentation accuracy of both cartilages and menisci.

The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images

Wenmiao Lu

Papers

Color filter array demosaicking: new method and performance measures

A color histogram based people tracking system

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies.

A vision-based approach to early detection of drowning incidents in swimming pools

The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images