• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.111-118
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• 2003

This paper proposes a two dimensional behavior analysis method for fish in a water tank based on the ARX method and the Kalman filter algorithm using image processing technology. In modeling the behavior of fish, the input is denoted as the environmental change and uses M-sequence. The output is expressed by the partnership between fish. The behavior model of individual fish is identified by the ARX method. It is then estimated by the Kalman filter algorithm. Finally, the fish behavior is analyzed by FFT. To prove the effectiveness of the pro-posed algorithm, it is applied to two tilapias in a water tank with dimensions of 100cm$\times$100cm$\times$50cm. The effectiveness of the proposed method is demonstrated through ARX identification, estimation of Kalman filter, and FFT analysis.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.222-225
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• 2000

Human frequently communicate non-linguistic information with gesture. So, we must develop efficient and fast gesture recognition algorithms for more natural human-computer interaction. However, it is difficult to recognize gesture automatically because human's body is three dimensional object with very complex structure. In this paper, we suggest a method which is able to detect key frames and frame changes, and to classify image sequence into some gesture groups. Gesture is classifiable according to moving part of body. First, we detect some frames that motion areas are changed abruptly and save those frames as key frames, and then use the frames to classify sequences. We symbolize each image of classified sequence using Principal Component Analysis(PCA) and clustering algorithm since it is better to use fewer components for representation of gestures. Symbols are used as the input symbols for the Hidden Markov Model(HMM) and recognized as a gesture with probability calculation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.114-125
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• 1998

In this study, for the first time, we propose the ADI(Adaptive De-Interlacing) algorithm, which improves visually and subjectively, horizontal and vertical edges on the image processed by the ELA (Edge Based Line Average) method. The proposed ADI algorithm enlargesthe window size to 5*3 in order to utilize the feature of the continuity of edges, and the adaptive interpolator is employed to decide adaptiely horizontal, diagonal, and vertical edges. Based on the results of the compter simulation, it is confimed that the new ADI algorithm improve the PSNR by 0.5dB in the Lena image with 512*512 size and by 0.4dB in the sequence image of a salesman, respectively. For the horizontal and vertial edges on the still and salesman sequence images, the proposed ADI algorithm has better visulal improvement than the conventional ELA algorithm.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.425-428
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• 2003

Recently, the IDS(Intrusion Detection System) using a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in the environment with abrupt illumination change. In this paper, we propose an effective moving edge extraction algorithm from a sequence image. The proposed algorithm extracts edge segments from current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of moving object in the noisy environment with abrupt illumination change.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.8
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• pp.1707-1714
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• 1997

This paper proposes a novel algorithm for fractal coding of image sequence, based on the CPM (Circular Prediction Mapping) and the NCIM (Non Contractive Interframe Mapping). In the CPM and the NCIM, each range block is approximated by a domain block in the adjacent frame, which is of the same size as the range block. Also, in this paepr, we propose a coding scheme of color components and an algorithm for controlling the bit rate, resepectively, for practical implementation of the fractal coder. The computer simulation results on real image sequences demonstrate that the proposed algorithm provides very promising performance at low bit-rate, below 256 Kbps.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.353-361
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• 2001

A new stereoscopic codec. structure using MPEG-2 multiview profile is presented in this paper. In the suggested codec., the left image is coded with motion estimation in the base layer and the right image is coded with disparity estimation in the enhancement layer. Since it is possible to calculate rough motion of the right image sequence with disparity and motion of the left image sequence, motion compensation of the enhancement layer is performed without motion estimation. To apply this mathod to MVP codec., the prediction mode of base layer and enhancement layer is restricted, and B picture mode in the base layer is removed. Since the proposed codec. does not perform motion estimation in the enhancement layer encoding and prediction mode of base layer is restricted, it's structure is simple and reduces the encoding time. We compared the SNR of encoded image with three different structured codec., and the experimental results show suggested codec. have comparable result.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.183-188
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• 2010

A factorization-based 3D reconstruction system is realized to recover 3D scene from an image sequence. The image sequence is captured from uncalibrated perspective camera from several views. Many matched feature points over all images are obtained by feature tracking method. Then, these data are supplied to the 3D reconstruction module to obtain the projective reconstruction. Projective reconstruction is converted to Euclidean reconstruction by enforcing several metric constraints. After many triangular meshes are obtained, realistic reconstruction of 3D models are finished by texture mapping. The developed system is implemented in C++, and Qt library is used to implement the system user interface. OpenGL graphics library is used to realize the texture mapping routine and the model visualization program. Experimental results using synthetic and real image data are included to demonstrate the effectiveness of the developed system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.