Milan Sulc

Fast Features Invariant to Rotation and Scale of Texture

A family of novel texture representations called Ffirst, the Fast Features Invariant to Rotation and Scale of Texture, is introduced. New rotation invariants are proposed, extending the LBP-HF features, improving the recognition accuracy. Using the full set of LBP features, as opposed to uniform only, leads to further improvement. Linear Support Vector Machines with an approximate \(\chi ^2\)-kernel map are used for fast and precise classification.

Texture-Based Leaf Identification

A novel approach to visual leaf identification is proposed. A leaf is represented by a pair of local feature histograms, one computed from the leaf interior, the other from the border. The histogrammed local features are an improved version of a recently proposed rotation and scale invariant descriptor based on local binary patterns (LBPs).

Kernel-mapped histograms of multi-scale LBPs for tree bark recognition

We propose a novel method for tree bark identification by SVM classification of feature-mapped multi-scale descriptors formed by concatenated histograms of Local Binary Patterns (LBPs). A feature map approximating the histogram intersection kernel significantly improves the methods accuracy. Contrary to common practice, we use the full 256 bin LBP histogram rather than the standard 59 bin histogram of uniform LBPs and obtain superior results. Robustness to scale changes is handled by forming multiple multi-scale descriptors. Experiments conducted on a standard dataset show 96.5% accuracy using ten-fold cross validation. Using the standard 15 training examples per class, the proposed method achieves a recognition rate of 82.5% and significantly outperforms both the state-of-the-art automatic recognition rate of 64.2% and human experts with recognition rates of 56.6% and 77.8%. Experiments on standard texture datasets confirm that the proposed method is suitable for general texture recognition.

Fine-grained recognition of plants from images

BackgroundFine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition “in the wild”.ResultsWe propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition “in the wild”.ConclusionsThe results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition “in the wild” where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Plant Identification: Experts vs. Machines in the Era of Deep Learning

Automated identification of plants and animals have improved considerably in the last few years, in particular thanks to the recent advances in deep learning. The next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree between each others when validating visual or audio observations of living organism. A picture or a sound actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This chapter reports an experimental study following this idea in the plant domain. In total, nine deep-learning systems implemented by three different research teams were evaluated with regard to nine expert botanists of the French flora. Therefore, we created a small set of plant observations that were identified in the field and revised by experts in order to have a near-perfect golden standard. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This shows that automated plant identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems.