An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

Transient thermal stresses due to axisymmetric heat supply in a semi-infinite thick circular plate

The present paper deals with the determination of thermal stresses in a semi-infinite thick circular plate of a finite length and infinite extent subjected to an axisymmetric heat supply. A thick circular plate is considered having constant initial temperature and arbitrary heat flux is applied on the upper and lower face. The governing heat conduction equation has been solved by using integral transform technique. The results are obtained in terms of Bessel’s function. The thermoelastic behavior has been computed numerically and illustrated graphically for a steel plate

An Ant Colony Optimization based Routing Techniques for VANET

With number of moving vehicles, vehicular Ad Hoc Network (VANET) is formed. These are provided with the wireless connections. Among various challenges in the VANET such as security and privacy of the messages, data forwarding is also considered as a major challenge. The effective communication is mainly depends on the how safely and fast the data is being forwarded among the vehicles. Data forwarding using Greedy mechanism suitable for routing in the VANETs, it depends only on the position of nodes and also data forwarding is done with minimum number of hops. In this paper, Position based GPCR and topology based DYMO routing protocol are adapted to make the use of Ant Colony Optimization (ACO) procedures. The resulting bio-inspired protocols, ACO_GPCR and ACO_DYMO had its performance evaluated and compared against existing GPCR and DYMO routing protocols. The obtained results suggest that making the use of ACO algorithm make these protocols more efficient in terms of Delay, Jitter, Packet Delivery Ratio and energy consumption

Morphometric studies in the genus Clerodendrum L.

Six Clerodendrum L. species from Kolhapur district were morphometrically analyzed with the help of PCA, cluster analysis and CD. It was observed that the quantitative characters viz. petiole length, leaf length and leaf width have great significance in delimitation of all the species and corolla tube length, leaf width, gynoecium length and leaf length have great contribution in separation of the taxa. Clerodendrum multiflorum (Burm.f.) O. Ktze.- Clerodendrum inerme (L.) Gaertn., Clerodendrum paniculatum L. - Clerodendrum viscosum Vent. and Clerodendrum inerme (L.) Gaertn. - Clerodendrum serratum (L.) Moon. are very closely related with each other and Clerodendrum multiflorum (Burm.f.) O.Ktze.- Clerodendrum paniculatum L. and Clerodendrum multiflorum (Burm.f.) O. Ktze. - Clerodendrum viscosum Vent. are significantly different from each other

Stock assessment of small head hair tail Eupleurograrnmus muticus (Gray) (Pisces/Trichiuridae) from Mumbai coast

Based on the data collected from New Ferry Wharf, Versova and Vasai in the years 1997-99, the age, growth, mortality and stock assessment of small head hair tail, Eupleurograrnmus muticus (Gray)is reported in the present communication. The growth parameters - asymptotic length (Lm) and growth coefficient (K) were estimated as 81 1 mm and 0.78 per year respectively. The average total, natural and fishing mortality coefficients were estimated as 4.36, 1.15 and 3.21 respectively. The yield isopleths diagram shows that cumetric fishing could be achieved at exploitation rate (E) of 0.68 and LC 1 Lm value of 0.68. The present E of 0.73 is well beyond the optimum E of 0.50. Thus some management measures should be taken to prevent depletion of this resource

Comparison of condition factor of the ribbonfish Lepturacanthus savala (Cuvier, 1829) and Eupleurogrammus muticus (Gray, 1831) from Mumbai coast

Two co-existing species of ribbonfish Lepturacanthus savala (Cuvier, 1829) and Eupleurogrammus muticus (Gray, 1831) were landed by traditional dol net and trawl net in Mumbai waters with the former contributed more in the landings. Fluctuations in the condition factor have been found in both the sexes of L. savala and E. muticus. k value of the former species was highly affected by gonadal maturation than feeding activity where as k value of latter species was highly linked up with feeding intensity than sexual maturity. Female specimens had higher condition factors than males in both the species

Thermal stresses in functionally graded hollow sphere due to non-uniform internal heat generation

In this article, the thermal stresses in a hollow thick sphere of functionally graded material subjected to non-uniform internal heat generation are obtained as a function of radius to an exact solution by using the theory of elasticity. Material properties and heat generation are assumed as a function of radius of sphere and Poisson’s ratio as constant. The distribution of thermal stresses for different values of the powers of the module of elasticity and varying power law index of heat generation is studied. The results are illustrated numerically and graphically

Classification of Rock Images using Textural Analysis

The classification of natural images is an useful task in current computer vision, pattern recognition applications etc. Rock images are a typical example of natural images, therefore their analysis is of major importance in the rock industry and in bedrock investigations. Rock image classification is based on specific textural descriptors which are extracted from the images. Using these descriptors, images are divided into various types. In the case of natural images, the feature distributions are often non-homogeneous and the image classes are also overlapping in the feature space. This can be problematic, if all the descriptors are combined into a single feature vector in the classification of an image. A method is presented for combining different visual descriptors in rock image classification. In this paper, k-nearest neighbor classification will be carried out for pair of descriptor separately. After that, the final decision is made by combining the results of each classification. The total numbers of the neighbors representing each class are used as votes in the final classification. The classification method will be tested using three types of rock. DOI: 10.17762/ijritcc2321-8169.15039