Browsing by Author "Hua, Kai-Lung"

Now showing 1 - 11 of 11

A Cloud-based Intelligent Skin and Scalp Analysis System
(2018-01-01)
Huang, Wen-Shiung
;
Hong, Bing-Kai
;
Cheng, Wen-Huang
;
Sun, Shih-Wei
;
Hua, Kai-Lung
The love of beauty is an essential part of all healthy human nature. Not only do women pay great attention to facial care, but in recent years, men's consumption in this area has also grown year by year. In facial care, in addition to sunscreen, skin care, tattoos and other chemical-based skin care products, mechanical skin washing machine has also become one of the most popular beauty appliances in recent years. In order to learn the effectiveness of various face care products and tools, in this paper, we have proposed an intelligent system that integrates face washing, wireless cameras, smart phones, and cloud image analysis functionality to allow users to obtain product recommendations. The proposed system is easy to carry and provides seven analysis functionalities, such as skin color, pigmentation, skin texture, wrinkles, pores, texture analysis, and would produce an in-depth analysis report. The proposed system is not only helpful for personal beauty care, but also beneficial for professional medical clinics and makeup companies. The collected big data will also be utilized as a reference for future cosmetics, and potential medical and biotechnology company product development and marketing.
A filter-based self-similar trace synthesizer
(2007-05-01)
Yao, Chien
;
Hua, Kai-Lung
;
Chen, Po-Ning
;
Chen, Jin-Yuan
;
Chiang, Tihao
Recent empirical studies have shown that modern computer network traffic is much more appropriately modelled by long-range dependent self-similar processes than traditional short-range dependent processes such as Poisson. Thus, if its selfsimilar nature is not considered in the synthesis of experimental network data, incorrect performance assessments for network systems may result. This raises the need of a self-similar trace synthesizing algorithm with long-range dependence. In this paper, we propose and examine the feasibility of a filter-based method for the synthesis of self-similar network traces. The proposed approach can alleviate the problems encountered by conventional synthesizers, such as random midpoint displacement and Paxson's spectrum fitting, which cannot generate self-similar traces on the fly and may give negative numbers. Additionally, the extended range of self-similarity of the filtered approach can be easily managed by the filter truncation window; therefore, a trace that faithfully matches the measured behavior of true network traffic, where the self-similar nature only lasts beyond a certain range but disappears as the considered aggregated window is much further extended, can be generated.
ADAPTING SEMANTIC SEGMENTATION OF URBAN SCENES VIA MASK-AWARE GATED DISCRIMINATOR
(2019-01-01)
Lin, Yong-Xiang
;
Tan, Daniel Stanley
;
Cheng, Wen-Huang
;
Hua, Kai-Lung
;
電子工程學系及電子研究所
Training a deep neural network for semantic segmentation relies on pixel-level ground truth labels for supervision. However, collecting large datasets with pixel-level annotations is very expensive and time consuming. One workaround is to utilize synthetic data where we can generate potentially unlimited data with their corresponding ground truth labels. Unfortunately, networks trained on synthetic data perform poorly on real images due to the domain shift problem. Domain adaptation techniques have shown potential in transferring the knowledge learned from synthetic data to real world data. Prior works have mostly leveraged on adversarial training to perform a global aligning of features. However, we observed that background objects have lesser variations across different domains as opposed to foreground objects. Using this insight, we propose a method for domain adaptation that models and adapts foreground objects and background objects separately. Our approach starts with a fast style transfer to match the appearance of the inputs. This is followed by a foreground adaptation module that learns a foreground mask that is used by our gated discriminator in order to adapt the foreground and background objects separately. We demonstrate in our experiments that our model outperforms several state-of-the-art baselines in terms of mean intersection over union (mIoU).
Domain Adaptation With Foreground/Background Cues and Gated Discriminators
(2020-07-01)
Lin, Yong-Xiang
;
Tan, Daniel Stanley
;
Chen, Yung-Yao
;
Huang, Ching-Chun
;
Hua, Kai-Lung
Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that background objects tend to be similar across domains, while foreground objects tend to have more variations. Using this insight, we propose an adaptation method that uses foreground and background cues and adapt them separately. We also propose a mask-aware gated discriminator that learns soft masks from the input foreground and background masks instead of naively performing binary masking that immediately removes information outside of the predicted masks. We evaluate our method on two different datasets and show that our method outperforms several state-of-the-art baselines, which verifies the effectiveness of our approach.
FUZZY PERSONALIZED SCORING MODEL FOR RECOMMENDATION SYSTEM
(2019-01-01)
Yang, Chao-Lung
;
Hsu, Shang-Che
;
Hua, Kai-Lung
;
Cheng, Wen-Huang
;
電子工程學系及電子研究所
In this research, we aim to propose a data preprocessing framework particularly for financial sector to generate the rating data as input to the collaborative system. First, clustering technique is applied to cluster all users based on their demographic information which might be able to differentiate the customers' background. Then, for each customer group, the importance of demographic characteristics which are highly associated with financial products purchasing are analyzed by the proposed fuzzy integral technique. The importance scores across items and customers are generated either on customer groups and individuals. The analysis shows the proposed method is able to differentiate customers based on their demographic and purchasing behaviors. Also, the generated rating matrix can be directly used for collaborative filtering model.
Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos
(2019-01-01)
Hidayati, Shintami C.
;
Hua, Kai-Lung
;
Tsao, Yu
;
Shuai, Hong-Han
;
Liu, Jiaying
Clothing image analysis has shown its potential for use in a wide range of applications such as personalized clothing recommendation. Given a consumer photo, this paper addresses the problem of finding clothes and recognizing the genre of that clothes. This problem is very challenging due to large variations of uncontrolled realistic imaging conditions. To tackle these challenges, we formulate a novel framework by integrating local features of multi-modality as the instances of the price-collecting Steiner tree (PCST) problem to discover clothing regions, and exploiting visual style elements to discover the clothing genre. The experimental results show that our fully automatic approach is effective to identify irregular shape of clothing region, and it significantly improves the accuracy of clothing genre recognition for images taken in unconstrained environment.
SEGMENTING HEPATIC LESIONS USING RESIDUAL ATTENTION U-NET WITH AN ADAPTIVE WEIGHTED DICE LOSS
(2019-01-01)
Liu, Yu-Cheng
;
Tan, Daniel Stanley
;
Chen, Jyh-Cheng
;
Cheng, Wen-Huang
;
Hua, Kai-Lung
We propose a novel network architecture called Residual Attention U-Net (ResAttU-Net) for segmenting hepatic lesions. Our model incorporates residual blocks that can extract more complex features as compared with traditional convolutional layers combined with a skip-connection attention module that learns to focus on the relevant features for the task of hepatic lesions segmentation. Moreover, we train our model using an adaptive weighted dice loss that prioritizes the pixels of the tumor class over the pixels of the background class. We evaluate our model on the MICCAI Liver Tumor Segmentation (LiTS) benchmark dataset. Our experimental results show that our method significantly improves upon several state-of-the-art baselines for hepatic lesion or liver tumor segmentation.
SPATIALLY-AWARE DOMAIN ADAPTATION FOR SEMANTIC SEGMENTATION OF URBAN SCENES
(2019-01-01)
Lin, Yong-Xiang
;
Tan, Daniel Stanley
;
Cheng, Wen-Huang
;
Chen, Yung-Yao
;
Hua, Kai-Lung
It is very expensive and time consuming to collect a large enough dataset with pixel-level annotations to train a semantic segmentation model. Synthetic datasets are common alternatives for training segmentation models, however models trained on synthetic data do not necessarily perform well on real world images due to the domain shift problem. Domain adaptation techniques address this problem by leveraging on adversarial training to align features. Prior works have mostly performed global feature alignment. They do not consider the positions of objects. However, objects in urban scenes are highly correlated with their spatial locations. For example, the sky will always appear on top while cars will usually appear in the middle of the image. Based on this insight, we propose a spatial-aware discriminator that accounts for the spatial prior on the objects in order to improve the feature alignment. We demonstrate in our experiments that our model outperforms several state-of-the-art baselines in terms of mean intersection over union (mIoU).
VEHICLE DETECTION IN THERMAL IMAGES USING DEEP NEURAL NETWORK
(2018-01-01)
Chang, Chin-Wei
;
Srinivasan, Kathiravan
;
Chen, Yung-Yao
;
Cheng, Wen-Huang
;
Hua, Kai-Lung
In today's world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. We propose a real-time vehicle detection using a sequence of night-time thermal images. Moreover, the thermal images have the capability of retaining even the minuscule vehicle details in a dim environment. For an efficient vehicle detection, the thermal image dataset collected during the dusk and night is used for training purposes. Subsequently, the contrast enhancement and sharpening of these images are performed using the Thermal Feature Enhancement (TFE). Then the concatenated images are supplied as the input to allow the model to learn more effectively. Besides, we also propose an improved convolution network model entitled as the Thermal Image Only Looked Once (TOLO) model for vehicle detection. Additionally, we propose a method called as Low Probability Candidate Filter (LPCF) to compensate the probability of not-easy-to-detect vehicles. Our proposed method produces better results for the F1-measure in comparison with existing methods.
What Dress Fits Me Best? Fashion Recommendation on the Clothing Style for Personal Body Shape
(2018-01-01)
Hidayati, Shintami Chusnul
;
Hsu, Cheng-Chun
;
Chang, Yu-Ting
;
Hua, Kai-Lung
;
Fu, Jianlong
Clothing is an integral part of life. Also, it is always an uneasy task for people to make decisions on what to wear. An essential style tip is to dress for the body shape, i.e., knowing one's own body shape (e.g., hourglass, rectangle, round and inverted triangle) and selecting the types of clothes that will accentuate the body's good features. In the literature, although various fashion recommendation systems for clothing items have been developed, none of them had explicitly taken the user's basic body shape into consideration. In this paper, therefore, we proposed a first framework for learning the compatibility of clothing styles and body shapes from social big data, with the goal to recommend a user about what to wear better in relation to his/her essential body attributes. The experimental results demonstrate the superiority of our proposed approach, leading to a new aspect for research into fashion recommendation.
ZipNet: ZFNet-level Accuracy with 48 x Fewer Parameters
(2018-01-01)
Antioquia, Arren Matthew C.
;
Tan, Daniel Stanley
;
Azcarraga, Arnulfo
;
Cheng, Wen-Huang
;
Hua, Kai-Lung
With the introduction of Convolutional Neural Networks, models for image classification achieve higher classification accuracy. Based on the pattern of the design of CNN architectures, increasing the number of layers equates to a higher classification accuracy, but also increases the number of parameters and model size. This negatively affects the model training time, processing time, and memory requirement. We develop ZipNet, a CNN architecture with a higher classification accuracy than ZFNet, the winner of ILSVRC 2013, but with 48.5x smaller model size and 48.7x fewer parameters. The classification accuracy of ZipNet is higher than the performance of ZFNet and SqueezeNet on all configurations of the Caltech-256 dataset with varying number of training examples.