Italy




This paper describes the analysis of defocus information when a finger touching a virtual keyboard by using DCT (Discrete Cosine Transform) coefficient without detecting 3D position (especially exact depth value) of thr finger. We use 2 cameras and a half mirror to get the images of the finger with same optical axis. The focal length of the two cameras are slightly different. We can know the finger touching the virtual keyboard when the finger is in the middle of the two focal length from the defocus information, because the position of the virtual keyboard is in the middle of the two focal position where the defocus information of the two cameras are same. Previous work of a virtual keyboard is realized by detecting the feature point of the finger at first. After that the finger position and the depth are checked if they are same to the position and the depth of the keyboard. A problem of the previous virtual keyboard is 3D point of the finger (feature point or edge, small region) should be detected. This is very time consuming. We overcome this problem by comparing the defocus information of the finger in the key area of virtual keyboard on the 2 images captured by the two cameras. In this paper we describe the optical system of the virtual keyboard and the resolution of the depth of the finger. Experimental result make it clear that middle range of the DCT coefficients is effective for detecting the finger. In our system, the finger touch is detected by comparing the DCT coefficients of the 2 images. The minimum distance of the detectable finger is depended on the diameter and the focal length of the lens, the resolution of the image sensor. We formulate this theoretical minimum distance and verify it by experiment. Besides finger, our system can also be generalized to detect other object touching the keyboard or not.

ICPR 2020

Image Defocus Analysis for Finger Detection on A Virtual Keyboard

virtual keyboard; depth estimation from focus; dct;

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



Differential models for the simulation of the muscle mechanics are based on iteratively updating a mesh grid and deducing its new state through a finite element model. Models usually assume that the mesh grid is almost regular, and this makes a degradation of the simulation accuracy in long simulation sequences, since the mesh tends to be less regular when the number of iterations increases. We present a model that has the aim of reducing this accuracy degradation. It is based on recomputing the mesh grid returned by the model in each iteration through the concept of graph matching. The new model is currently in use to analyse the dynamics of the human heart when some pressure is applied to it. The final goal of the project (which is not shown in this paper) is to deduce the optimal position and strength pressure applied to the heart that increases the chance of reviving it with the minimum tissue damage. Experimental validation shows that our model returns a higher accuracy of the muscle position through some iterations than classical differential models with an insignificant increase of runtime. Thus, it is worth recomputing the mesh grid since the simulation accuracy drastically increases at the expense of a low runtime increase.

Incorporating a graph-matching algorithm into a muscle mechanics model





We present SIMCO, a completely agnostic multi-class object counting approach.
SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (circle, square, rectangle, etc.). Each object detected is described by a low-dimensional embedding, obtained from a novel similarity-based head branch; this latter implements a triplet loss, encouraging similar objects (same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this embedding for clustering, so that different 'classes' of similar objects can emerge and be counted, making SIMCO the very first multi-class unsupervised counter. The only required assumption is that repeated objects are present in the image. Experiments show that SIMCO provides state-of-the-art scores on counting benchmarks and that it can also help in many challenging image understanding tasks.

SIMCO: SIMilarity-based object COunting




Attributed graphs are structures that are useful to represent objects through the information of their local parts and their relations. Each characteristic in the local parts is represented by different attributes on the nodes. In this context, the comparison between structured objects is performed through a distance between attributed graphs. If we want to correctly tune the distance and the node correspondence between graphs, we have to add some weights on the node attributes to gauge the importance of each local characteristic. In this paper, we present a method to learn the weights on each node attribute. It is based on building an embedded space and imposing the weights we want to learn to be the constants of the hyperplane deduced by a linear regression applied on a cloud of points. These points represent the node-to-node mappings.

Learning Graph Matching Substitution Weights based on a Linear Regression

CNN Based Predictor of Face Image Quality

Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems in real life. To resolve this issue, we formulate the crack detection problem as a weakly-supervised problem and propose a two-branched framework. By combining predictions of a supervised model trained on low quality annotations with predictions based on pixel brightness, our framework is less affected by the annotation quality. Experimental results show that the proposed framework retains high detection accuracy even when provided with low quality annotations.

Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors

Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Evaluation of Interpretable Association Rule Mining Methods on time-series in the Maritime Domain

The quality of uni-directional tape in its production
process is affected by environmental conditions like temperature
and production speed. In this paper, computer vision algorithms
on the scanned images are needed to be used in this context
to detect and classify tape damages during the manufacturing
procedure. We perform a comparative study among famous
feature descriptors for fault candidate generation, then propose
own features for fault detection. We investigate various machine
learning techniques to find best model for the classification problem. The empirical results demonstrate the high performance of
the proposed system and show preference of random forest and
canny edges for classifier and feature generator respectively.

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Image Defocus Analysis for Finger Detection on A Virtual Keyboard

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