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In this paper, an algorithm is introduced for segmenting the foreground regions present in a human insulin crystal intensity image captured by an in-situ microscope inside of a bioreactor. The segmentation is carried out by classifying all image pixels into pixels belonging to the foreground regions and pixels belonging to the background region. For classification, the local intensity variance at...
The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive...
Video search today uses the metadata surrounding the video, ignoring its semantic content. Over the years, a lot of research has gone into indexing and browsing of sports video content. In this work, we present a novel approach for classification of events in cricket videos and thus, summarize its visual content. The proposed method segments a cricket video into shots and identifies the visual content...
This paper proposes a general benchmark for interactive segmentation algorithms. The main contribution can be summarized as follows: (I) A new dataset of fifty images is released. These images are categorized into five groups: animal, artifact, human, building and plant. They cover several major challenges for the interactive image segmentation task, including fuzzy boundary, complex texture, cluttered...
This paper introduces a system that automatically classifies image pairs based on the type of registration required to align them. The system uses support vector machines to classify between panoramas, high-dynamic-range images, focal stacks, super-resolution, and unrelated image pairs. A feature vector was developed to describe the images, and 1100 pairs were used to train and test the system with...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
The proposed classifier is a novel skin detector that outperforms most of the existing approaches by dropping most of the non-skin pixels in its earlier stages of weak classifiers. Only the pixels with maximum skin likelihood are processed in later adaptive classifier. Parametric background modelling and validation based online training significantly improves the robustness of the whole classifier...
As texture information among pixels can be effectively represented using Local binary patterns (LBPs), image descriptors built using LBPs or its variants have been frequently used for various image analysis applications, e.g. medical image and texture image classification and retrieval. However, neither LBP nor any of its existing variants can be used to build descriptors for classifying multimodal...
This paper presents an algorithm for localization and classification of subtitles in TV videos. We extend an existing static-region detector with object-based adaptive filtering and binary classification of subtitle bounding boxes, using geometry and text-stroke alignment features. Compared to this static-region detector, we reduce the number of falsely detected subtitle pixels by a factor of 20,...
A Research of maize disease image recognition of corn leaf based on image processing and analysis, which is to study diseases of image classification. According to the texture characteristics of corn diseases, it uses YCbCr color space technology to segment disease spot, and uses the cooccurrence matrix spatial gray level layer to extract disease spot texture feature, and uses BP neural network to...
For distinguish the LSB (Least significant bit) replacement stego image from MLSB (Multiple least significant bits) stego image, which are two typical kinds of steganographical methods of image spatial domain and have been applied widely, a classification algorithm based on the shift of pixel value and irrelevance of pixel pairs is proposed. In this algorithm, a shift operator is adopted for each...
Designing static object detection systems that are able to incorporate user interaction conveys a great benefit in many surveillance applications, since some correctly detected static objects can be considered to have no interest by a human operator. Interactive systems allow the user to include these decisions into the system, making automated surveillance systems more attractive and comfortable...
Detecting static objects in video sequences has a high relevance in many surveillance scenarios like airports and railwaystations. In this paper we propose a system for the detection of static objects in crowded scenes that, based on the detection of two background models learning at different rates, classifies pixels with the help of a finite-state machine. The background is modelled by two mixtures...
We address the problem of recognizing actions in reallife videos. Space-time interest point-based approaches have been widely prevalent towards solving this problem. In contrast, more spatially extended features such as regions have not been so popular. The reason is, any local region based approach requires the motion flow information for a specific region to be collated temporally. This is challenging...
A classification-based trained motion adaptive temporal filtering scheme was proposed. The proposed method calculates class code and assigns optimal temporal filter coefficients based on pixel-wise temporal movement. The proportional-integral-derivative (PID) control concept was newly applied to enhance classifier. The proposed method involves integral and derivative terms of temporal movement as...
In this paper, a new classification scheme of fully polarimetric SAR images is proposed. This is based on the joint use of the Freeman-Durden decomposition and generalized discriminant analysis, a new method for Feature extraction. After getting the powers of the three scattering mechanism components through Freeman-Durden decomposition, the Feature extraction algorithm is introduced to well exploit...
Airborne Light Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy...
Experts-Shift is a novel statistical framework for keyframe-based video segmentation. Compared to existing video segmentation techniques with simple color models, our method proposes a probability mixture model coupling strong image classifiers (experts) with latent spatial configuration. In order to propagate image labels to the successive frames, our algorithm track all experts jointly by a efficient...
Vega has been widely used in Virtual Reality (VR) field. Vega infrared (IR) module can implement IR simulation, but Vega IR imaging simulation's general approach does not apply to large-scale scene's infrared simulation problem. This article deeps into large-scale scene IR image simulation through Vega infrared module based on visible image, the scene's corresponding Digital Elevation Model (DEM)...
Acute lymphoblastic leukemia (ALL) is the most common hematological neoplasia of childhood and is characterized by uncontrolled growth of leukemic cells in bone marrow, lymphoid organs etc. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of...
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