62 citations · 154 across the 18 of their papers we have counts for
18 papers
Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation
Lukman Hakim, Takio Kurita
The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation tha…
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels
Huipeng Zheng, Lukman Hakim, Takio Kurita +1
The deep learning technique was used to increase the performance of single image super-resolution (SISR). However, most existing CNN-based SISR approaches primarily focus on establ…
Attention-effective multiple instance learning on weakly stem cell colony segmentation
Novanto Yudistira, Muthu Subash Kavitha, Jeny Rajan +1
The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentat…
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor Embedding
Motoshi Abe, Junichi Miyao, Takio Kurita
The dimensionality reduction has been widely introduced to use the high-dimensional data for regression, classification, feature analysis, and visualization. As the one technique o…
Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN
Novanto Yudistira, Muthu Subash Kavitha, Takio Kurita
3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the in…
Channel Planting for Deep Neural Networks using Knowledge Distillation
Kakeru Mitsuno, Yuichiro Nomura, Takio Kurita
In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computat…
Filter Pruning using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks
Kakeru Mitsuno, Takio Kurita
Since the convolutional neural networks are often trained with redundant parameters, it is possible to reduce redundant kernels or filters to obtain a compact network without dropp…
Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection
Shah B. Shrey, Lukman Hakim, Muthusubash Kavitha +2
Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule iden…
U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image
Lukman Hakim, Novanto Yudistira, Muthusubash Kavitha +1
The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body. Existin…
Triplet Loss for Knowledge Distillation
Hideki Oki, Motoshi Abe, Junichi Miyao +1
In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes la…
Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural Network
Motoshi Abe, Junichi Miyao, Takio Kurita
A deep convolutional neural network (CNN) has been widely used in image classification and gives better classification accuracy than the other techniques. The softmax cross-entropy…
Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks
Kakeru Mitsuno, Junichi Miyao, Takio Kurita
In a deep neural network (DNN), the number of the parameters is usually huge to get high learning performances. For that reason, it costs a lot of memory and substantial computatio…
On-line non-overlapping camera calibration net
Zhao Fangda, Toru Tamaki, Takio Kurita +2
We propose an easy-to-use non-overlapping camera calibration method. First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames. Nex…
Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural Network
Hideki Oki, Takio Kurita
The deep Convolutional Neural Network (CNN) became very popular as a fundamental technique for image classification and objects recognition. To improve the recognition accuracy for…
Correlation Net: Spatiotemporal multimodal deep learning for action recognition
Novanto Yudistira, Takio Kurita
This paper describes a network that captures multimodal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over a multimodal…
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
Jin Yamanaka, Shigesumi Kuwashima, Takio Kurita
We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have…
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
Shohei Kumagai, Kazuhiro Hotta, Takio Kurita
This paper proposes a crowd counting method. Crowd counting is difficult because of large appearance changes of a target which caused by density and scale changes. Conventional cro…
Domain Adaptation with L2 constraints for classifying images from different endoscope systems
Toru Tamaki, Shoji Sonoyama, Takio Kurita +8
This paper proposes a method for domain adaptation that extends the maximum margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2 distance constraints between…