• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.66-77
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• 2011

We propose a framework for grouping photographic advertising images that employs a hierarchical indexing scheme based on efficient feature combinations. The study provides one specific application of effective tools for monitoring photographic advertising information through online and offline channels. Specifically, it develops a preprocessor for advertising image information tracking. We consider both global features that contain general information on the overall image and local features that are based on local image characteristics. The developed local features are invariant under image rotation and scale, the addition of noise, and change in illumination. Thus, they successfully achieve reliable matching between different views of a scene across affine transformations and exhibit high accuracy in the search for matched pairs of identical images. The method works with global features in advance to organize coarse clusters that consist of several image groups among the image data and then executes fine matching with local features within each cluster to construct elaborate clusters that are separated by identical image groups. In order to decrease the computational time, we apply a conventional clustering method to group images together that are similar in their global characteristics in order to overcome the drawback of excessive time for fine matching time by using local features between identical images.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2739-2749
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• 1996

This aper introduces the multiple Neocognitron module approach for the effective target reognition. The Neocognitron which is designed to classify a pattern by extracting the local features from it, seems to be an unique method that can perform a pattern recognition using the neural networks. But due to its rigid structure, the Neocognitron must be reconstructed whenever there exists a variation on the number of classes. This is a quite difficult problem for the target recognition application that needs huge amount of computation and numerous classes to be classified. In this paper, we construct several smaller Necognitrom modules and train each module to adapt each class. After construction of the mulules, we integrate them in parallel so as to adaopt input at the same time and to produce each score that shold be matched to be learned class. This approach can reduce the sizes of the networks and is adaptive to the increase of classes as well as the authentic distortion, shift, scale variation and slight rotation invariant properties of general Neocognitron. This paper show the effectiveness of the proposed approach through some experience and performs analysis of the inhibitory interconnections in the architecture of the multiple module structure.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.2
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• pp.157-169
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• 2013

Scale Invariant Feature Transform (SIFT) generates image features widely used to match objects in different images. Previous work on hardware-based SIFT implementation requires excessive internal memory and hardware logic [1]. In this paper, a new hardware organization is proposed to implement SIFT with less memory and hardware cost than the previous work. To this end, a parallel Gaussian filter bank is adopted to eliminate the buffers that store intermediate results because parallel operations allow all intermediate results available at the same time. Furthermore, the processing order is changed from the raster-scan order to the block-by-block order so that the line buffer size storing the source image is also reduced. These techniques trade the reduction of memory size with a slight increase of the execution time and external memory bandwidth. As a result, the memory size is reduced by 94.4%. The proposed hardware for SIFT implementation includes the Descriptor generation block, which is omitted in the previous work [1]. The addition of the hardwired descriptor generation improves the computation speed by about 30 times when compared with the previous work.

• ETRI Journal
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• v.45 no.6
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.935-943
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• 1993

In this paper, robust recognition system for tilted facial image was developed. At first, standard facial image and lilted facial image are captured by CCTV camera and then transformed into binary image. The binary image is processed in order to obtain contour image by Laplacian edge operator. We trace and delete outermost edge line and use inner contour lines. We label four inner contour lines in order among the inner lines, and then we extract left and right eye with known distance relationship and with two eyes coordinates, and calculate slope information. At last, we rotate the tilted image in accordance with slope information and then calculate the ten distance features between element and element. In order to make the system invariant to image scale, we normalize these features with distance between left and righ eye. Experimental results show 88% recognition rate for twenty five face images when tilted degree is considered and 60% recognition rate when tilted degree is not considered.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.800-807
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• 2016

We propose a fast algorithm using Extreme Contour Point (ECP) to detect the angle of rotated images, is implemented by rotation feature of one covered frame image that can be applied to correct the rotated images like in image processing for real time applications, while CORDIC is inefficient to calculate various points like high definition image since it is only possible to detect rotated angle between one point and the other point. The two advantages of this algorithm, namely compatibility to images in preprocessing by using Sobel edge process for pattern recognition. While the other one is its simplicity for rotated angle detection with cyclic shift of two $1{\times}n$ matrix set without complexity in calculation compared with CORDIC algorithm. In ECP, the edge features of the sample image of gray scale were determined using the Sobel Edge Process. Then, it was subjected to binary code conversion of 0 or 1 with circular boundary to constitute the rotation in invariant conditions. The results were extracted to extreme points of the binary image. Its components expressed not just only the features of angle ${\theta}$ but also the square of radius $r^2$ from the origin of the image. The detected angle of this algorithm is limited only to an angle below 10 degrees but it is appropriate for real time application because it can process a 200 degree with an assumption 20 frames per second. ECP algorithm has an O ($n^2$) in Big O notation that improves the execution time about 7 times the performance if CORDIC algorithm is used.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1176-1194
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• 2018

The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.