Italy


Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics.
In this poster, we introduce Recursive Convolutional Neural Networks (RCNN) for the analysis of epigenomic data.
We focus on the task of predicting gene expression from the intensity of histone modifications.
The proposed RCNN architecture can be applied to data of an arbitrary size, and has a single meta-parameter that quantifies the models capacity, thus making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems, while having several orders of magnitude fewer parameters.

ICPR 2020

Recursive Convolutional Neural Networks for Epigenomics

epigenomic study

recursive cnn

biological image and signal analysis

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


poster


Automated tongue recognition is a growing research field due to global demand for personal health care. Using mobile devices to take tongue pictures is convenient and of low cost for tongue recognition. It is particularly suitable for selfhealth evaluation of the public. However, images taken by mobile devices are easily affected by various imaging environment, which makes fine segmentation a more challenging task compared with those taken by specialized acquisition devices. Deep learning approaches are promising for tongue image segmentation because they have powerful feature learning and representation capability. However, the successive pooling operations in these methods lead to loss of information on image details, making them fail when segmenting low-quality images captured by mobile devices. To address this issue, we propose a dilated encoder network (DE-Net) to capture more high-level features and get high-resolution output for automated tongue image segmentation. In addition, we construct two tongue image datasets which contain images taken by specialized devices and mobile devices, respectively, to verify the effectiveness of the proposed method. Experimental results on both datasets demonstrate that the proposed method outperforms the state-ofthe-art methods in tongue image segmentation.

DE-Net: Dilated Encoder Network for Automated Tongue Segmentation


The success of single image super-resolution reconstruction (SR) relies on a refined mapping from low-resolution (LR) examples to high-resolution (HR) signals. However, the relation is sometimes chaos, especially in a deep SR network. We try to improve the mapping interpretability in two folds: i) The variable local dense blocks (VLDB) are suggested to match receptive fields in different depths of a residual dense network (RDN), with each VLDB a dyadic increment of layer numbers than its predecessor. ii) Based on VLDBs, a dense in dense (DID) network is created. It substitutes nodes in a regular RDN with super nodes, i.e. VLDBs; and formulates a joint learning by flexible hierarchical feature scaling, reusing and long-short term aggregating. VLDBs deal with feature underfitting occurred when a big receptive field meets a fixed-depth dense block, and the DID network provides a relative complete feature dictionary to preserve details for feature shift, dilating and grouping in high dimension image reconstruction. To demonstrate the validness of DID structure, detail experiments are performed on the benchmark datasets Set5, Set14, B100 and Urban100, where the accuracy PSNR and the visual perceptive SSIM are superior to most state-of-the-art methods. Besides, due to the depth adaption of VLDBs and its nesting in generalized RDN，DID network is converged easily and gradient explosion or disappearance are alleviated even when network deepens.

DDID: A Nested Dense in Dense Structure with Variable Local Dense Blocks for Super-Resolution Image Reconstruction

Video-to-video face recognition in unconstrained conditions is still a very challenging problem, as the combination of several factors leads to an in general low-quality of facial frames. Besides, in some real contexts, the availability of labelled samples is limited, or data is streaming or it is only available temporarily due to storage constraints or privacy issues. In these cases, dealing with learning as an unsupervised incremental process is a feasible option.

This work proposes a system based on dynamic ensembles of SVM's, which uses the ideas of self-training to perform adaptive Video-to-video face identification. The only label requirements of the system are a few frames (5 in our experiments) directly taken from the video-surveillance stream. The system will autonomously use additional video-frames to update and improve the initial model in an unsupervised way. Results show a significant improvement in comparison to other state-of-the-art static models.

An Adaptive Video-to-Video Face Identification System Based on Self-Training

In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array.
Human body estimation from such a single LFIR image is a hard task.
For training the estimation model, annotation of the human pose to the images is also a difficult task for humans. 
Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training.
Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference.
Through an experiment, we evaluated the human pose estimation accuracy with an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

LFIR2Pose : Pose Estimation from an Extremely Low-Resolution FIR Image Sequence

Late fusion (LF) architectures are common approaches in classification tasks that consist of several feature subspaces, e.g. multi-modal sensor data, such as a combination of audio, video, and physiological signals. In the current study, we propose a trainable sample-specific LF architecture that combines classification and weighting models (CMs, WMs). The idea of our approach is to train each WM in combination with a set of CM-specific meta labels.

Using Meta Labels for the Training of Weighting Models in a Sample-Specific Late Fusion Classification Architecture

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.

Human or Machine? It Is Not What You Write, But How You Write It

We present an approach to detect basic movements of cyclists in real-world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide-angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639
video sequences containing cyclists, recorded in real-world traffic, resulting in over 1.1 million samples. Through modeling the cyclists’ behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not
limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame-wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position-based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.

Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution

Object detection in images has reached unprecedented performances. The state-of-the-art methods rely on deep architectures that extract salient features and predict bounding boxes enclosing the objects of interest. These methods essentially run on RGB images. However, the RGB images are often compressed by the acquisition devices for storage purpose and transfer efficiency. Hence, their decompression is required for object detectors. To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions.
Specifically, we focus on JPEG images and propose a thorough analysis of detection architectures newly designed in regard of the peculiarities of the JPEG norm. This leads to a x1.7 speed up in comparison with a standard RGB-based architecture, while only reducing the detection performance by 5.5%. Additionally, our empirical findings demonstrate that only part of the compressed JPEG information, namely the luminance component, may be required to match detection accuracy of the full input methods.

Object Detection in the DCT Domain: is Luminance the Solution?


The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. The conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.

Fusion of Global-Local Features for Image Quality inspection of Shipping Label


Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the 
increasing number of applications based on wide angle capture devices, mainly generating 360o spherical images, have enforced the need for dedicated superpixel approaches. In this paper, we introduce a new superpixel method for spherical images called SphSPS (for Spherical Shortest Path-based Superpixels). Our approach respects the spherical geometry and generalizes the notion of shortest path between a pixel and a superpixel center on the 3D spherical acquisition space. To relevantly evaluate this last aspect in the spherical space, we also generalize a planar global regularity metric. Finally, the proposed SphSPS method obtains significantly better performances than both planar and spherical recent superpixel approaches on the reference 360o spherical panorama segmentation dataset.

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