Approximation of the Euclidean Distance by Chamfer Distances

Hajdú, András; Hajdu, Lajos; Tijdeman, R.

doi:10.14232/actacyb.20.3.2012.3

Cited by 13 publications

(10 citation statements)

References 19 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use a symmetric version of the Chamfer distance [10], calculated as the sum of the average minimum distance from point set A to point set B and vice versa. The average minimum distance from one point set to another is calculated as the average of the distances between the points in the first set and their closest point in the second set, and is thus not symmetrical.…”

Section: Attention Based Generatormentioning

confidence: 99%

“…In particular if the distance measure accurately captures the notion of user quality, then small differences in the distance should not matter. [10] search returns points, whose distance from the query is at most c times the distance from the query to its nearest points.…”

Section: Similarity Searchmentioning

confidence: 99%

“…For our experiments, we evaluate the generative quality with Chamfer Distance [10] (CD). We estimate CD using 1024 randomly sampled points on the ground truth and generated systems.…”

Section: Metricsmentioning

confidence: 99%

“…We estimate CD using 1024 randomly sampled points on the ground truth and generated systems. We have tested the Chamfer Distance [10] on a series of viral Proteins of the Severe acute respiratory syndrome coronavirus 2. Table 2: Visual Representation of the produced results.…”

Section: Metricsmentioning

confidence: 99%

See 3 more Smart Citations

De Novo Drug Design using Artificial Intelligence ASYNT-GAN

Jacobs¹,

Μaragoudakis²

2020

Preprint

View full text Add to dashboard Cite

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

show abstract

Section: Attention Based Generatormentioning

confidence: 99%

Section: Similarity Searchmentioning

confidence: 99%

“…For our experiments, we evaluate the generative quality with Chamfer Distance [10] (CD). We estimate CD using 1024 randomly sampled points on the ground truth and generated systems.…”

Section: Metricsmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

See 2 more Smart Citations

De Novo Drug Design using Artificial Intelligence ASYNT-GAN

Jacobs¹,

Μaragoudakis²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…But it is impossible because of the irregularity of trabecular bone. Another feasible method, distance transform, was put forward by Rosenfield and Pfaltz in [23] and was widely used in image understanding, pattern recognition, and computer vision [4,8,14,17,30].…”

Section: Chamfer Distance Transformmentioning

confidence: 99%

Effective identification and localization of immature precursors in bone marrow biopsy

Cao

Chen

et al. 2014

Med Biol Eng Comput

View full text Add to dashboard Cite

Abnormal localization of immature precursors (ALIP) aggregating and clustering in bone marrow biopsy appears earlier than that of bone marrow smears in detection of the relapse of acute myelocytic leukemia (AML). But traditional manual ALIP recognition has many shortcomings such as prone to false alarms, neglect of distribution law before three immature precursor cells gathered, and qualitative analysis instead of quantitative one. So, it is very important to develop a novel automatic method to identify and localize immature precursor cells for computer-aided diagnosis, to disclose their patterns before ALIP with the development of AML. The contributions of this paper are as follows. (1) After preprocessing the image with Otsu method, we identify both precursor cells and trabecular bone by multiple morphological operations and thresholds. (2) We localize the precursors in different regions according to their distances with the nearest trabecular bone based on chamfer distance transform, followed by discussion for the presumptions and limitations of our method. The accuracy of recognition and localization is evaluated based on a comparison with visual evaluation by two blinded observers.

show abstract