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Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches...
A smart camera processor has to perform substantial amount of processing of data-intensive operations. Hence, it is vital to identify critical segments of the processing load by involving HW/SW codesign in smart camera system design. This paper presents a novel fully automatic hybrid framework that combines heuristic and knowledge-based approaches to partition, allocate and schedule IP modules efficiently...
The `fuzzy co-clustering algorithm for images (FCCI)' technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives...
The success of visual tracking systems is highly dependent upon the effectiveness of the measurement function used to evaluate the likelihood of a hypothesized object state. Generative tracking algorithms attempt to find the global and other local maxima of these measurement functions. As such, designing measurement functions which have a small number of local maxima is highly desirable. Edge based...
The inherent nature of image and video and its multi-dimension data space makes its processing and interpretation a very complex task, normally requiring considerable processing power. Moreover, understanding the meaning of video content and storing it in a fast searchable and readable form, requires taking advantage of image processing methods, which when running them on a video stream per query,...
Many computer vision algorithms such as object tracking and event detection assume that a background model of the scene under analysis is known. However, in many practical circumstances it is unavailable and must be estimated from cluttered image sequences. We propose a sequential technique for background estimation in such conditions, with low computational and memory requirements. The first stage...
An abandoned object detection system is presented and evaluated using benchmark datasets. The detection is based on a simple mathematical model and works efficiently at QVGA resolution at which most CCTV cameras operate. The pre-processing involves a dual-time background subtraction algorithm which dynamically updates two sets of background, one after a very short interval (less than half a second)...
This paper proposes automated detection of skin lesions by unsupervised feature based clustering based on a new fuzzy entropy function for characterizing texture. The parameterized entropy function is optimized using the Bacterial Foraging algorithm. The clustering of the entropy function of the image is done using the popular Fuzzy C-means algorithm (FCM). The experimental results obtained after...
We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated...
This paper describes a visual surveillance system for remote monitoring of unattended environments. For the purpose of efficiently tracking multiple people in the presence of occlusions, we propose: (i) to combine blob matching with particle filtering, and (ii) to augment these tracking algorithms with a novel colour appearance model. The proposed system efficiently counteracts the shortcomings of...
Under the constraint of using only a single gallery image per person, this paper proposes a fast multi-class pattern classification approach to 2D face recognition robust to changes in pose, illumination, and expression (PIE). This work has three main contributions: (1) we propose a representative face space method to extract robust features, (2) we apply a learning method to weight features in pairs,...
A novel modification of the Fuzzy Clustering for Categorical Multivariate date (FCCM) algorithm termed as dasiaFuzzy Co-Clustering Algorithm for Images (FCCI)psila is proposed for clustering of medical images. The main aim of this work is to segment regions of interest in histo-pathological images which consist of groups of similar cells indicating some form of abnormality in the animal tissue. The...
Although automatic identity inference based on faces has shown success when using high quality images, for CCTV based images it is hard to attain similar levels of performance. Furthermore, compared to recognition based on static images, relatively few studies have been done for video based face recognition. In this paper, we present an empirical analysis and comparison of face recognition using high...
Intelligent video surveillance is currently a hot topic in computer vision research. The goal of intelligent video surveillance is to process the captured video from the monitored area, extract specific information and take appropriate action based on that information. Due to the high computational complexity of vision tasks and the real-time nature of these systems, current software-based intelligent...
Smart cameras are rapidly finding their way into intelligent surveillance systems. Recognizing faces in the crowd in real-time is one of the key features that will significantly enhance intelligent surveillance systems. The main challenge is the fact that the high volumes of data generated by high-resolution sensors make it computationally impossible for mainstream computers to process. In our proposed...
Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy...
Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, adaptive principal component analysis (APCA) (Blanz and Vetter, 1999), which performs well against both lighting variation and...
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