International journal of imaging and robotics 

About: International journal of imaging and robotics is an academic journal. The journal publishes majorly in the area(s): Image segmentation & Segmentation. Over the lifetime, 179 publications have been published receiving 770 citations.

A computer vision-based method for fire detection in color videos

Abstract: This paper presents a computer vision-based approach for automatically detecting the presence of fire in video sequences. The algorithm not only uses the color and movement attributes of fire, but also analyzes the temporal variation of fire intensity, the spatial color variation of fire, and the tendency of fire to be grouped around a central point. A cumulative time derivative matrix is used to detect areas with a high frequency luminance flicker. The fire color of each frame is aggregated in a cumulative fire color matrix using a new color model which considers both pigmentation values of the RGB color and the saturation and the intensity properties in the HSV color space. A region merging algorithm is then applied to merge the nearby fire colored moving regions to eliminate the false positives. The spatial and temporal color variations are finally applied to detect fires. Our extensive experimental results demonstrate that the proposed system is effective in detecting all types of uncontrolled fire in various situations, lighting conditions, and environment. It also performs better than the peer system with higher true positives and true negatives and lower false positives and false negatives.

Color Based Image Retrieval using Amendment of Block Truncation Coding with YCbCr Color Space

Abstract: Nanotechnologies may change to the better many sectors of industry, but considerable concern is arising about their side effects and possible risks to human life. The potential toxicity of nanoparticles (NPs) versus cells has to be much more clearly investigated than it has been done to date to define their future role in biological, medical and environmental applications. The present study performed in-vitro standardized cytotoxicity tests using Hematite, Magnetite and Valentinite nanoparticles with 3T3 cells. Biological (XTT and Brd-U assays), morphological (ESEM and TEM) and physical (EDS and x-ray diffraction) investigations were performed to evaluate cell-nanoparticle interaction and physical state after interaction. The results identified a threshold toxic concentration for all NPs, beyond which no cytotoxic effects were detectable by standardized tests. Notwithstanding these results, cytoplasmatic and nuclear translocation was observed and verified also during mitotic phase. The limits of the standardized tests are analyzed and discussed.

Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook

Abstract: Image retrieval has become imperative area of research because of vide range of applications needing the image data search facility. Most of the research approaches in the area are either database based indexing or image processing based CBIR. The hours need is to combine these parallel going approaches of research to have better image retrieval techniques. The paper proposes a novel technique for image retrieval using the color-texture features extracted from images based on the color indexing using vector quantization. This gives better discrimination capability for CBIR. Here we are dividing the database image into 2x2 pixel windows to obtain 12 color descriptors (Per pixel Red, Green and Blue) per row of window table. Then the Kekre’s Median Codebook Generation (KMCG) is applied on window table to get 256 centre rows. The DCT is applied on this centre row vector to obtain feature set of size 256x12, which is user for image retrieval. The method takes fewer computations as compared to conventional DCT applied on complete image. The method gives the color-texture features of the image database at reduced feature set size.

Scaling Invariant Fusion of Image Pieces In Panorama Making And Novel Image Blending Technique

Abstract: The panorama making using fusion of partial image pieces of desired view has been active areas of research in recent years. The image panoramas primarily aim to enhance field of view. Image fusion plays important role to create full view panoramic mosaics from sequences of smaller picture parts. Each smaller part is stitched together to get panorama. Image Stitching is used to construct an image with a large field of view than that could be obtained with a single photograph. It refers to transformation and fusion of multiple images into a new aggregate image without any visible seam or distortion in the overlapping areas. The important step in making panoramas is automatic estimation of the overlap. Overlap is the common region in consecutive picture parts. Overlap boundary indicates where one image ends and the other image begins. These images should be combined in such a way that the final image does not have any spurious artificial edges. The partial images may differ in their size and brightness. Panorama making fuses image parts together by transforming all of them to same row-size and then blends them together to minimize the brightness differences.

Automatic Recognition of Off-line Handwritten Arabic (Indian) Numerals Using Support Vector and Extreme Learning Machines

Abstract: This paper describes a technique using Support Vector (SVM) and Extreme Learning Machines (ELM) for automatic recognition of off-line handwritten Arabic (Indian) numerals. The features of angle, distance, horizontal, and vertical span are extracted from these numerals. The database has 44 writers with 48 samples of each digit totaling 21120 samples. A two-stage exhaustive parameter estimation technique is used to estimate the best values for the SVM parameters for this application. For SVM parameter estimation, the database is split into 4 subsets: three were used in training and validation in turn, and the fourth for testing. The SVM and ELM classifiers were trained with 75% of the data (i.e. the first 33 writers) and tested with the remaining data (i.e. writers 34 to 44) by using the estimated parameters. The recognition rates of SVM and ELM classifiers at the digit and writers’ levels are compared. The training and testing times of SVM and ELM indicate that ELM is much faster to train and test than SVM. The classification errors are analyzed and categorized. The recognition rates of SVM and ELM classifiers are compared with Hidden Markov Model (HMM) and the Nearest Mean (NM) classifiers. Using the SVM, ELM, HMM and NM classifiers the achieved average recognition rates are 99.39%, 99.45%, 97.99% and 94.35%, respectively. ELM and SVM recognition rates are better for all the digits than HMM and NM. The presented technique, using simple features and SVM and ELM classifiers, are effective in the recognition of handwritten Arabic (Indian) numerals, and it is shown to be superior to HMM and NM classifiers for all digits.