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To provide an effective way for product modeling design, a product modeling design method based on the fusion of texture and shape features and computer technology is proposed. Based on the product modeling design drawing. By constructing the image texture gray-level co-occurrence matrix and extracting the image texture primitives, the energy, inertia, entropy, and evenness statistics of the texture are obtained, which serve to describe the image texture characteristics. The OHTA color model is employed to segment the shape and background of the product design drawing, while the Fourier descriptor is utilized to obtain the shape features. Based on the texture and shape features required for product modeling design, the image required for product modeling design is retrieved from the image database by calculating the similarity between the texture and shape features and the image feature vector in the image database. Using the retrieved image as input, the framework of the product modeling design virtual environment is first established, and subsequently, the product modeling design is implemented within this virtual environment using Rhino 3D software. The experiment shows that the texture and shape features extracted by this method are more accurate, and can effectively retrieve the image needed for product modeling design from the image database according to the texture and shape features. Based on this image, the product modeling design is realized, and the application effect is relatively remarkable.

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

In this paper, we propose a simple, but efficient method to recognize two-dimensional shapes without regard to their translation, rotation, and scaling factors. In our scheme, we use all of the boundary points to calculate the first principal component, which is the first shape feature. Next, by dividing the boundary points into groups by projecting them onto the first principal component, each shape is partitioned into several blocks. These blocks are processed separately to produce the remaining shape features. In shape matching, we compare two shapes by calculating the difference between the two sets of features to see whether the two shapes are similar or not.

The amount of storage used to represent a shape in our method is fixed, unlike most other shape recognition schemes. The time complexity of our shape matching algorithm is also O(n), where n is the number of blocks. Therefore, the matching algorithm takes little computation time, and is independent of translation, rotation, and scaling of shapes.

In this short paper, we introduce a parameterized shape model, rose curve model. An analytical solution for estimating rose curve parameters from a binary silhouette or a probability map is derived. This analytical method finds the global optimum directly and therefore is fast and reliable. Two similarity invariant shape features, which measures the concavity and circular frequency of the shape can be derived from the six parameters of the rose curve. We apply the rose curve model to approximately segmenting flower images, primarily for testing the analytic parameter estimation method. Experiments on a database of 180 flower images from 30 species show that the rose curve is an excellent shape model for many flower species and the analytical parameter estimation method locates the flower regions well.

In this paper, a novel scheme has been proposed for image retrieval task using the feature extracted directly from a compressed or uncompressed image. The texture information is first extracted by exploiting the multiresolution nature of wavelet decomposition, which represent the horizontal, vertical and diagonal frequency distribution of an image. We then calculate the mean and standard deviation of wavelet coefficients of each sub-band as texture features. In additions, we also extract shape feature by using the fixed-resolution block representation, which divides the image into isometric blocks and calculate the overlapped degree of each block with binary codes. The experimental results show that the retrieval efficiency is considerably improved by the proposed approach.

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

Shape feature is a useful descriptor for object recognition. Wavelet decomposition is fit for shape feature extraction and image denoising. Then the object recognition can be executed by shape feature invariant moment distance. The experiments results for actual vehicle-logo images taken from traffic stations show that the method is practical and effective.

Basic shape features of objects in a binary image are very important for image analysis and pattern recognition. In conventional algorithms, for basic shape feature computation, a labeled image generated by a connected-component labeling processing is usually necessary. This paper proposes a fast two-scan algorithm for calculating shape features of objects in a binary image without the use of a labeled image. Experiments demonstrated that the proposed algorithm is much more efficient than conventional algorithms for calculating basic shape features of objects in a binary image.

This study is aimed to explore the different laws of visual perception on yachts by applying SMI RED 500 eye tracker device, combined with a subjective five-point scale questionnaire to evaluate the subject’s eye movement data when viewed through different forms of yachts. In this article, we research the extent of people’s preferences on yacht styling characteristics and the sequence of observation, and understand the extent of the influence between the yacht styling characteristics of various parts and overall shape, and analyze the styling features of cabin windows.