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Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object-(BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%-100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

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Development of techniques for music visualization is important and still open problem in analysis and creation of the quantitative profiles of single or multiple compositions, which could be used as required constraints in music generation or music classification processes. When generating creative data with no objective function, it is hard to select or to find appropriate measurable features. This paper proposes a method to normalize data in MIDI files by 12 dimensional vector descriptors extracted from tonality as well as a novel technique for dimensionality reduction and visualization of extracted music data by 3D projections is discussed. Employing a non-overlapping sliding window through the composition, the harmonic features are found in a music piece. Then a self-similarity matrix is computed using distance metrics to analyze and project the resulting 3D feature vectors. Three dimensional projection creates a quantitative profile of a composition, which correlates the tone similarities along the music piece. The dimensionality reduction is compared with well-known autoencoder. Conducted tests show that our method preserves up to 90% of original data in the projection of reduced dimension. The advantages of the proposed method consist in a novel technique that provides interactive visualization and dynamically adjusts different metrics to observe the behavior of data during music information retrieval and recognition.

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