Sequential effects of environmental stimuli are ubiquitous in most behavioral tasks involving magnitude estimation, memory, decision making, and emotion. The human visual system exploits continuity in the visual envir...
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Sequential effects of environmental stimuli are ubiquitous in most behavioral tasks involving magnitude estimation, memory, decision making, and emotion. The human visual system exploits continuity in the visual environment, which induces two contrasting perceptual phenomena shaping visual perception. Previous work reported that perceptual estimation of a stimulus may be influenced either by attractive serial dependencies or repulsive aftereffects, with a number of experimental variables suggested as factors determining the direction and magnitude of sequential effects. Recent studies have theorized that these two effects concurrently arise in perceptual processing, but empirical evidence that directly supports this hypothesis is lacking, and it remains unclear whether and how attractive and repulsive sequential effects interact in a trial. Here we show that the two effects concurrently modulate estimation behavior in a typical sequence of perceptual tasks. We first demonstrate that observers’ estimation error as a function of both the previous stimulus and response cannot be fully described by either attractive or repulsive bias but is instead well captured by a summation of repulsion from the previous stimulus and attraction toward the previous response. We then reveal that the repulsive bias is centered on the observer’s sensory encoding of the previous stimulus, which is again repelled away from its own preceding trial, whereas the attractive bias is centered precisely on the previous response, which is the observer’s best prediction about the incoming stimuli. Our findings provide strong evidence that sensory encoding is shaped by dynamic tuning of the system to the past stimuli, inducing repulsive aftereffects, and followed by inference incorporating the prediction from the past estimation, leading to attractive serial dependence.
Eye semantic segmentation is a fundamental task in many works such as identification and medical applications. In this study, three encoder-decoder architectures using convolutional neural network are applied to segme...
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ISBN:
(纸本)9781450372633
Eye semantic segmentation is a fundamental task in many works such as identification and medical applications. In this study, three encoder-decoder architectures using convolutional neural network are applied to segment the eyes. A simple encoder-decoder architecture is capable of generating only coarse segmentation results. On the other hand, fine details like eyelashes can be achieved by U-net and SegNet architectures. However, they sometimes produce overall results worse than the simple one. To resolve this problem, we introduce a deep convolutional neural network-based ensemble technique for eye segmentation. The results from those architectures are combined in order to yield good results in both coarse-level and fine-level segmentation. In the proposed technique, a trainable mask function is applied to achieve an optimal ensemble of coarse-level and fine-level results. Our dataset comprises 64 eye images from different environments, camera settings, people, and eye conditions. Experimental results show that our ensemble technique can improve the results from the conventional architectures. The proposed ensemble method manages to reach the average accuracy of 96.33% for three-class segmentation.
Many computer vision applications rely on segmentation task. To achieve a good result on Handwritten text recognition (HTR), character segmentation is significant in terms of extracting each individual character. In t...
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ISBN:
(纸本)9781450372633
Many computer vision applications rely on segmentation task. To achieve a good result on Handwritten text recognition (HTR), character segmentation is significant in terms of extracting each individual character. In this study, we propose a novel algorithm for tackling offline handwritten character segmentation, particularly for the Thai language. Not only are the characteristics of the Thai language described, but also the problems when performing Thai character segmentation are defined. There are two parts of segmentation: horizontal link segmentation and vertical link segmentation. The chosen type of algorithm is convolutional encoder-decoder network. Our models are based on the renowned encoder-decoder models, U-net and SegNet. The best horizontal link segmentation model achieves up to 0.929 F1-score on the real-world test set. For the vertical link segmentation, the best models of topmost, upper, base, and lower characters attains F1-scores of 0.799, 0.873, 0.932, and 0.820, respectively.
In this paper, we propose a non-task-oriented dialogue system controlling the utterance length. The dialogue system can be classified into a task-oriented dialogue system or a non-task-oriented dialogue system. Recent...
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ISBN:
(纸本)9781538626344;9781538626337
In this paper, we propose a non-task-oriented dialogue system controlling the utterance length. The dialogue system can be classified into a task-oriented dialogue system or a non-task-oriented dialogue system. Recently, demand for the non-task-oriented dialogue system is increasing. The utterance length is one of the important information in a dialogue system. In general, our utterance length tends to be long when we are speakers. On the other hand, the length of our utterance tends to be short when we are listeners. In addition, the utterance length differs from person to person, so we change our utterance length for friendly communication. The effect of the utterance length has never considered in dialogue systems using encoder-decoder model. Therefore, we propose an utterance length estimator (ULE) and an index of the utterance length. ULE is a neural network which learns the utterance length by training data of dialogue. The index of the utterance length is the parameter considers user's personality and it is calculated during dialogue. Our dialogue system decides the length of system's utterance by ULE and index of the utterance length, and generates output sequences by using a neural encoder-decoder controlling output length. Experimental results show our system can decide the appropriate length of the utterance and makes users more satisfied than the conventional method.
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