This paper addresses two problems: the automated pollen species recognition and counting them on an image obtained with a lighting microscope. Automation of pollen recognition is required in several domains, including allergy and asthma prevention in medicine and honey quality control in the nutrition industry. We propose a deep learning solution based on a convolutional neural network for classification, feature extraction and image segmentation. Our approach achieves state-of-theart results in terms of accuracy. For 5 species, the approach provides 99.8% of accuracy, for 11 species — 95.9%.
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019.
The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named:
Part I: best ranked papers; machine learning; pattern recognition; image processing and representation.
Part II: biometrics; handwriting and document analysis; other applications.
Binary energy optimization is a popular approach for segmenting an image into foreground/background regions. To model region appearance, color, a relatively high dimensional feature, should be handled effectively. A full color histogram is usually too sparse to be reliable. One approach is to reduce dimensionality by color space clustering. Another popular approach is to fit GMMs for soft color space clustering. These approaches work well when the foreground/background are sufficiently distinct. In cases of more subtle difference in appearance, both approaches may reduce or even eliminate foreground/background distinction. This happens because either color clustering is performed completely independently from segmentation, as a preprocessing step (in clustering), or independently for the foreground and independently for the background (in GMM). We propose to make clustering an integral part of segmentation, by including a new clustering term in the energy. Our energy favors clusterings that make foreground/ background appearance more distinct. Exact optimization is not feasible, therefore we develop an approximate algorithm. We show the advantage of including the color clustering term into the energy function on camouflage images, as well as standard segmentation datasets.
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016.
The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life and social sciences; morphological and technological approaches to image analysis.
The Shape Boltzmann Machine (SBM) and its multilabel version MSBM have been recently introduced as deep generative models that capture the variations of an object shape. While being more flexible MSBM requires datasets with labeled parts of the objects for training. In the paper we present an algorithm for training MSBM using binary masks of objects and the seeds which approximately correspond to the locations of objects parts. The latter can be obtained from part-based detectors in an unsupervised manner. We derive a latent variable model and an EM-like training procedure for adjusting the weights of MSBM using a deep learning framework. We show that the model trained by our method outperforms SBM in the tasks related to binary shapes and is very close to the original MSBM in terms of quality of multilabel shapes.
In the paper we address a challenging problem of incorporating preferences on possible shapes of an object in a binary image segmentation framework. We extend the well-known conditional random fields model by adding new variables that are responsible for the shape of an object. We describe the shape via a flexible graph augmented with vertex positions and edge widths. We derive exact and approximate algorithms for MAP estimation of label and shape variables given an image. An original learning procedure for tuning parameters of our model based on unlabeled images with only shape descriptions given is also presented. Experiments confirm that our model improves the segmentation quality in hard-to-segment images by taking into account the knowledge about typical shapes of the object.
Parameters that affect the perception quality of visual data has been investigated. Evaluation of such parameters due to distortion during filtering was determined. Segmentation methods according to colour and brightness similarity were discussed. Perceptive model for contrast sensitivity influence evaluation was discussed. The image region detection method for watermarking is suggested.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018. The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.