• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.161-172
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• 2021

Neuromorphic architecture is drawing attention as a next-generation computing that supports artificial intelligence technology with low energy. However, FPGA embedded boards based on Neuromorphic architecturehave limited resources due to size and power. In this paper, we compared and evaluated the image reduction method using the interpolation method that rescales the size without considering the feature points and the DCT (Discrete Cosine Transform) method that preserves the feature points as much as possible based on energy. The scaled images were compared and analyzed for accuracy through CNN (Convolutional Neural Networks) in a PC environment and in the Nengo framework of an FPGA embedded board.. As a result of the experiment, DCT based classification showed about 1.9% higher performance than that of interpolation representation in both CNN and FPGA nengo environments. Based on the experimental results, when the DCT method is used in a limited resource environment such as an embedded board, a lot of resources are allocated to the expression of neurons used for classification, and the recognition rate is expected to increase.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.133-149
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• 1996

In order to reduce the trial-and-errors in design and production of injection molded plastic parts, there has been much research effort not only on CAE systems which simulate the injection molding process, but also on CAD systems which support initial design and re-design of plastic parts and their molds. The CAD systems and CAE systems have been developed independently with being built on different basis. That is, CAD systems manipulate the part shapes and the design features in a complete solid model, while CAE systems work on shell meshes generated on the abstract sheet model or medial surface of the part. Therefore, it is required to support the two types of geometric models and feature information in one environment to integrate CAD and CAE systems for accelerating the design speed. A feature-based non-manifold geometric modeling system has been developed to provide an integrated environment for design and analysis of injection molding products. In this system, the geometric models for CAD and CAE systems are represented by a non-manifold boundary representation and they are merged into a single geometric model. The suitable form of geometric model for any application can be extracted from this model. In addition, the feature deletion and interaction problem of the feature-based design system has been solved clearly by introducing the non-manifold Boolean operation based on 'merge and selection' algorithm. The sheet modeling capabilities were also developed for easy modeling of thin plastic parts.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.1-21
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• 2024

This study investigates the characteristics of visual working memory (VWM) representations by examining two theoretical models: the integrated-object and the parallel-independent feature storage models. Experiment I involved a change detection task where participants memorized arrays of either orientation bars, colored squares, or both. In the one-feature condition, the memory array consisted of one feature (either orientations or colors), whereas the two-feature condition included both. We found no differences in change detection performance between the conditions, favoring the integrated object model over the parallel-independent feature storage model. Experiment II employed a recall task with memory arrays of isosceles triangles' orientations, colored squares, or both, and one-feature and two-feature conditions were compared for their recall performance. We found again no clear difference in recall accuracy between the conditions, but the results of analyses for memory precision and guessing responses indicated the weak object model over the strong object model. For ongoing debates surrounding VWM's representational characteristics, these findings highlight the dominance of the integrated object model over the parallel independent feature storage model.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.481-488
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• ETRI Journal
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• v.32 no.5
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.134-140
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• 2008

Preceding vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based preceded vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an preceded vehicle detection system is developed using stereo vision sensors. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the preceded vehicles including a leading vehicle. Then, the position parameters of the preceded vehicles or leading vehicles can be obtained. The proposed preceded vehicle detection system is implemented on a passenger car and its performances is verified experimentally.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2884-2896
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• 1998

For on-line signature verification, the local shape of a signature is an important information. The current approaches, in which signatures are represented into a function of time or a feature vector without regarding of local shape, have not used the various features of local shapes, for example, local variation of a signer, local complexity of signature or local difficulty of forger, and etc. In this paper, we propose a new technique for on-line signature verification based on a structural signature representation so as to analyze local shape and to make a selection of important local parts in matching process. That is. based on a structural representation of signature, a technique of important of local weighting and personalized decision threshold is newly introduced and its experimental results under different conditions are compared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1700-1721
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• 2017

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.