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Search Results: 1 - 10 of 200550 matches for " Channayya P. Hiremath "
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Induced genetic variability and correlation studies for yield and its component traits in Groundnut (Arachis hypogaea L.)
Channayya P. Hiremath , H. L. Nadaf and Keerthi,C.M
Electronic Journal of Plant Breeding , 2011,
Abstract: Groundnut is one of the principal economic oilseed crops of the world, which has been exposed extensively to mutagenictreatments for induction of genetic variability. In the present experiment, estimates of genetic variability, heritability and geneticadvance were assessed for 12 different quantitative traits in the mutants derived from two Spanish Bunch groundnut cultivars, viz.TPG-41 and GPBD-4 with chemical and physical mutagenic agents. Wide genetic variations were observed for most of thequantitative traits studied as evidenced by higher mean, range, PCV and GCV values. Further genetic improvement throughselection for yield improvement should rely on number of primary branches per plant, 100-kernel weight, SMK% and shellingper cent as these mutants recorded higher genetic variability, heritability and genetic advance for these quantitative traits. Podyield was positively and significantly associated with number of primary branches, pod weight per plant, 100-kernel weight,sound matured per cent kernel and oil yield. These results clearly indicate that idirect selection for yield in groundnut is possiblethrough simultaneous improvement of these yield components
Assessment of genetic diversity among germplasm lines of horsegram (Macrotyloma uniflorum ) at Bijapur
B.G. Prakash, Channayya. P. Hiremath, S.B. Devarnavdgi and P.M. Salimath
Electronic Journal of Plant Breeding , 2010,
Abstract: In Northern dry zone of Karnataka lot of variability exists in horse gram crop as many farmers are growing local cultivarswhich are resistant to iron chlorosis and also to diseases and pest with high forage yield. In the present study, an attempt wasmade to assess the genetic divergence among the 100 germplasm lines using Mahalanobis D2 statistic collected fromdifferent sources including local checks and the experiment was carried out during late Kharif season of 2009 at RARS,Bijapur. The observations were recorded on seed yield and its components. The 100 germplasm lines that were grouped intoeighteen different clusters based on D2 analysis revealed that Cluster I was the largest with 19 genotypes followed by clusterIII (14) and cluster V (13). Cluster XII showed the maximum mean value for seed yield. The intra and inter clusterdivergence among the genotypes was varying in magnitude. Further it was implied that intra-cluster distance was maximumin cluster III followed by clusters XI and XIII. The widest inter cluster distance was noted between cluster XII and XVgiving scope for hybridization programme with improvement of genotypes. The distance between clusters X and V wasminimum indicating close relationship between those clusters.
Genetic Diversity Analysis of Indian Mustard (Brassica juncea L.)
R.Doddabhimappa, B.Gangapur, G. Prakash and Channayya. P. Hiremath
Electronic Journal of Plant Breeding , 2010,
Abstract: Genetic diversity using Mahalanobis D2 statistics was studied in 46 genotypes of Indian mustard in both protected (againstdiseases and pests) and unprotected conditions for seed yield and its components. The 46 genotypes were grouped into sevenclusters based on D2 analysis in both conditions. Cluster III was the largest with 18 genotypes followed by cluster IV (12)and cluster I (5) in protected condition. In unprotected condition, cluster II comprised of 19 genotypes followed by cluster III(10) and cluster I, IV (5). The contribution of genotypes in different clusters was almost same in both the conditions whichstrengthened the conformation of formation of clusters. For both the conditions, it was observed that the same character,number of primary branches per plant exhibited maximum percentage towards divergence. The study revealed that theclusters I and VII possessing high mean values for the most of the desirable traits in both the conditions are desired to becrossed with cluster II and V for exploiting heterosis.
Implications of heterosis and combining ability among productive Single cross hybrids in tomato
L.Sekhar, B. G. Prakash, P. M. Salimath, Channayya. P. Hiremath, O. Sridevi and A. A. Patil
Electronic Journal of Plant Breeding , 2010,
Abstract: Ten tomato commercial and productive single cross hybrids extensively grown in Northern Transitional Zone of Karnatakawere planted in the field at UAS, Dharwad following RBD design with three replications. A 10 x 10 diallel set wasgenerated by crossing these single cross hybrids in all possible combinations (excluding reciprocals) and 45 double crosshybrids were planted during February, 2007 with three replications with a view to estimate heterosis and combining abilityto facilitate identification of heterosis combinations for all the ten characters studied. The range of heterosis (%) over midparent and better parent was wide for number of clusters per plant and number of locules per fruits as compared to othercharacters. The number of significant heterosis hybrids in desirable direction for both mid parent (28 hybrids) and betterparent( 24 hybrids) was highest for number of locules per fruit followed by number of cluster per plant (mid parent-17hybrids, better parent-11 hybrids). The overall gca and sca status for SCH and DCH respectively revealed that among singlecross hybrids JK-Desi was the best general combiner for yield and most of the traits followed by Pragathi and Maharani. Outof top five double cross hybrids, only two hybrids viz., JK-Desi x Sasya and JK-Desi x Shivaji expressed significant highpositive heterosis over mid-parent and better parent along with better performance in term of yield per plant. It isnoteworthy to mention that three of the five top double cross hybrids had JK-Desi as one of the common parent which ispotential donor for yield per plant, number of fruits per plant, number of branch per plant, plant height and pericarpthickness.
3D Face Recognition Using Radon Transform and Symbolic PCA
P. S. Hiremath,Manjunath Hiremath
International Journal of Electronics and Computer Science Engineering , 2012,
Abstract: Three Dimensional (3D) human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. Range images have several advantages over 2D intensity images and 3D meshes. Range images are robust to the change of color and illumination, which are the causes for limited success in face recognition using 2D intensity images. In the literature, there are several methods for face recognition using range images, which are focused on the data acquisition and preprocessing stage only. In this paper, a new 3D face recognition technique based on symbolic Principal Component Analysis approach is presented. The proposed method transforms the 3D range face images using radon transform and then obtain symbolic objects, (i.e. interval valued objects) termed as symbolic 3D range faces. The PCA is employed to symbolic 3D range face image dataset to obtain symbolic eigen faces which are used for face recognition. The proposed symbolic PCA method has been successfully tested for 3D face recognition using Texas 3D Face Database. The experimental results show that the proposed algorithm performs satisfactorily with an average accuracy of 97% as compared to conventional PCA method and is efficient in terms of accuracy and detection time.
International Journal of Machine Intelligence , 2011,
Abstract: Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extractingthe face region from the background. It also has several applications in areas such as content-based image retrieval, videocoding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. In this paper, we propose a novelapproach for the detection of human face in a digital image based on the fuzzy spatial interrelationships of only the prominentfacial features of the face, namely, eyes and mouth. A fuzzy face model is constructed for the face detection algorithm. Theexperimentation has been done using several face databases. The experimental results show that the proposed algorithm performssatisfactorily with an average accuracy of 96.10% and is efficient in terms of accuracy and detection time despite the exclusion ofother facial features, namely, nose, eyebrows and ears.
Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image.
Hiremath P. S,Jagadeesh Pujari
International Journal of Image Processing , 2008,
Abstract: Salient points are locations in an image where there is a significant variation withrespect to a chosen image feature. Since the set of salient points in an imagecapture important local characteristics of that image, they can form the basis of agood image representation for content-based image retrieval (CBIR). Salientfeatures are generally determined from the local differential structure of images.They focus on the shape saliency of the local neighborhood. Most of thesedetectors are luminance based which have the disadvantage that thedistinctiveness of the local color information is completely ignored in determiningsalient image features. To fully exploit the possibilities of salient point detection incolor images, color distinctiveness should be taken into account in addition toshape distinctiveness. This paper presents a method for salient pointsdetermination based on color saliency. The color and texture information aroundthese points of interest serve as the local descriptors of the image. In addition,the shape information is captured in terms of edge images computed usingGradient Vector Flow fields. Invariant moments are then used to record theshape features. The combination of the local color, texture and the global shapefeatures provides a robust feature set for image retrieval. The experimentalresults demonstrate the efficacy of the method.
Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement
Jagadeesh Pujari,P. S. Hiremath
International Journal of Computer Science and Security , 2007,
Abstract: Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency using image and its complement. The image and its complement are partitioned into non-overlapping tiles of equal size. The features drawn from conditional co-occurrence histograms between the image tiles and corresponding complement tiles, in RGB color space, serve as local descriptors of color and texture. This local information is captured for two resolutions and two grid layouts that provide different details of the same image. An integrated matching scheme, based on most similar highest priority (MSHP) principle and the adjacency matrix of a bipartite graph formed using the tiles of query and target image, is provided for matching the images. Shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the color and texture features between image and its complement in conjunction with the shape features provide a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
International Journal of Machine Intelligence , 2011,
Abstract: Accurate and reliable segmentation is an essential step in determining valuable quantitative information on size, shape and texture, which may assist microbiologists in their diagnoses. The snakes or active contours are used extensively in computer vision and image processing applications, particularly to locate object boundaries. The objective of the present study is to develop an automatic tool to identify and classify the virus particles in digital microscopic images using multigrid active contour model. Geometric features are used to identify the different types of virus particles, namely, Rotavirus and Adenovirus using 3 classifier, K-NN classifier and Neural Network classifiers. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for virus particle classification by segmenting digital microscopic virus images and extracting geometric features for virus particle classification. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method.
Extraction of Flat and Nested Data Records from Web Pages
P.S Hiremath,Siddu P. Algur
International Journal on Computer Science and Engineering , 2010,
Abstract: This paper studies the problem of identification and extraction of flat and nested data records from a given web page. With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, it is useful to mine such data regions and data records in order to extract information from such web pages to provide value-added services. Currently available automatic techniques to mine data regions and data records from web pages are still unsatisfactory because of their poor performance. In this paper a novel method to identify and extract the flat and nested data records from the web pages automatically is proposed. It comprises of two steps : (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification and extraction of flat and nested data records from the data region of a web page automatically. For step1, a novel and more effective method is proposed, which finds the data regions formed by all types of tags using visual clues. For step2, a more effective and efficient method namely, Visual Clue based Extraction of web Data (VCED), is proposed, which extracts each record from the data region and identifies it whether it is a flat or nested data record based on visual clue information the area covered by and the number of data items present in each record. Our experimental results show that the proposed technique is effective and better than existing techniques.
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