Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

Illuminant Estimation by Voting

Obtaining an estimate of the illuminant color is an important component in many image analysis applications. Due to the complexity of the problem many restrictive assumptions are commonly applied, making the existing illuminant estimation methodologies not widely applicable on natural images. We propose a methodology which analyzes a large number of regions in an image. An illuminant estimate is obtained independently from each region and a global illumination color is computed by consensus. Each region itself is mainly composed by pixels which simultaneously exhibit both diffuse and specular reflection. This allows for a larger inclusion of pixels than purely specularitybased methods, while avoiding, at the same time, some of the restrictive assumptions of purely diffuse-based approaches. As such, our technique is particularly well-suited for analyzing real-world images. Experiments with laboratory data show that our methodology outperforms 75 % of other illuminant estimation methods. On natural images, the algorithm is very stable and provides qualitatively correct estimates. 1

Complex identification of plants from leaves

Se presenta una propuesta para el reconocimiento de hojas muy semejantes en su apariencia.The automatic identification of plant leaves is a very important current topic of research in vision systems. Several researchers have tried to solve the problem of identification from plant leaves proposing various techniques. The proposed techniques in the literature have obtained excellent results on data sets where the leaves have dissimilar features to each other. However, in cases where the leaves are very similar to each other, the classification accuracy falls significantly. In this paper, we proposed a system to deal with the performance problem of machine learning algorithms where the leaves are very similar. The results obtained show that combination of different features and features selection process can improve the classification accuracy

COLOR TEXTURED IMAGE RETRIEVAL BY COMBINING TEXTURE AND COLOR FEATURES

International audienceA new approach for color textured image retrieval based on the combination of color and texture features is proposed. The features are extracted in DCT domain. For texture featuring, Texture-Pattern is proposed to be constructed by using three groups of AC coefficients of each DCT block from the luminance component. And for color featuring, LumaColor-Pattern is constructed by using the DC coefficients from the luminance and chroma components. The histograms of dominant components of these two patterns are constructed and their combination is used for image retrieval. Experimental results on VisTex database have shown that the proposed method yields higher performance than referred approaches which are reported in recently published literature

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

A Method Of Content-based Image Retrieval For The Generation Of Image Mosaics

An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation process, several parameters are used within the system. Each parameter affects the overall mosaic quality, as well as required processing time, in its own unique way. A detailed analysis is performed to evaluate each parameter individually. Additionally, this work proposes two novel ways by which to evaluate the quality of an image mosaic in a quantitative way. One method focuses on the perceptual color accuracy of the mosaic reproduction, while the other concentrates on edge replication. Both measures include preprocessing to take into account the unique visual features present in an image mosaic. Doing so minimizes quality penalization due the inherent properties of an image mosaic that make them visually appealing