An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

From The late 90th, "Skin Detection" becomes one of the major problems in image processing. If "Skin Detection" will be done in high accuracy, it can be used in many cases as face recognition, Human Tracking and etc. Until now so many methods were presented for solving this problem. In most of these methods, color space was used to extract feature vector for classifying pixels, but the most of them have not good accuracy in detecting types of skin. The proposed approach in this paper is based on "Color based image retrieval" (CBIR) technique. In this method, first by means of CBIR method and image tiling and considering the relation between pixel and its neighbors, a feature vector would be defined and then with using a training step, detecting the skin in the test stage. The result shows that the presenting approach, in addition to its high accuracy in detecting type of skin, has no sensitivity to illumination intensity and moving face orientation.Comment: 9 Pages, 4 Figure

Phase-Retrieved Tomography enables imaging of a Tumor Spheroid in Mesoscopy Regime

Optical tomographic imaging of biological specimen bases its reliability on the combination of both accurate experimental measures and advanced computational techniques. In general, due to high scattering and absorption in most of the tissues, multi view geometries are required to reduce diffuse halo and blurring in the reconstructions. Scanning processes are used to acquire the data but they inevitably introduces perturbation, negating the assumption of aligned measures. Here we propose an innovative, registration free, imaging protocol implemented to image a human tumor spheroid at mesoscopic regime. The technique relies on the calculation of autocorrelation sinogram and object autocorrelation, finalizing the tomographic reconstruction via a three dimensional Gerchberg Saxton algorithm that retrieves the missing phase information. Our method is conceptually simple and focuses on single image acquisition, regardless of the specimen position in the camera plane. We demonstrate increased deep resolution abilities, not achievable with the current approaches, rendering the data alignment process obsolete.Comment: 21 pages, 5 figure

A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods

Vision-Based Production of Personalized Video

In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach

Supervised Content based Image Retrieval using Fuzzy Texton and Shearlet Transform

In this paper we proposed, a novel framework to assist and automate the diagnosis of diseases from computer-based image analysis method using Content-based image retrieval (CBIR). CBIR is the process of retrieving related images from large database collections by using low level image features such as color, texture and shape etc. we have used fuzzy texton and discrete shearlet transform to extract texture and shape features. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination with relevance feedback using Support Vector Machines

The TREC-2002 video track report

TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings